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Self-assessment about neuromyths

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Take our Myths & Facts self-assssment to test your own knowledge

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"In fact, multitasking is almost always a misnomer, as the human mind and brain lack the architecture to perform two or more tasks simultaneously."

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Editor's Note

What happens to your brain when you multitask? Does your brain slow down? Do you feel increased levels of stress? Why are some people better at it than others? Our authors supply the answers to some of these questions and provide the latest on what happens to the brain when you try to handle more than one task at a time.

As you go about your day, you may barely notice that you are frequently multitasking. It may be driving to work while listening to a radio program or talking to a loved one on the phone ( putting yourself and others at risk), or perusing Facebook while texting a friend, or switching back and forth between a high-level project like compiling a report and a routine chore like scheduling an appointment. Multitasking means trying to perform two or more tasks concurrently, which typically leads to repeatedly switching between tasks (i.e., task switching) or leaving one task unfinished in order to do another.

The scientific study of multitasking over the past few decades has revealed important principles about the operations, and processing limitations, of our minds and brains. One critical finding to emerge is that we inflate our perceived ability to multitask: there is little correlation with our actual ability. In fact, multitasking is almost always a misnomer, as the human mind and brain lack the architecture to perform two or more tasks simultaneously. By architecture, we mean the cognitive and neural building blocks and systems that give rise to mental functioning. We have a hard time multitasking because of the ways that our building blocks of attention and executive control inherently work. To this end, when we attempt to multitask, we are usually switching between one task and another. The human brain has evolved to single task.

Together with studies of patients who have suffered focal neural injuries, functional neuroimaging studies indicate that key brain systems involved in executive control and sustained attention determine our ability to multitask. These include the frontoparietal control network, dorsal attention network, and ventral attention network.

The first of these networks is thought to support the coding of a task goal and the selection of task-relevant information. This means identifying a task goal, selecting relevant information, and disregarding irrelevant information that does not help us to achieve the goal. In the context of multitasking, we know that the brain has a hard time processing and completing two or more tasks at once: the inherent ways the dorsal and ventral attention systems interact with the frontoparietal control network makes this so. When we approach a task, a goal representation in the frontoparietal control network is thought to guide top-down attentional allocation, mediated by the dorsal attention network, to select information that is relevant to achieve the task goal. This information can include external sensory information or internal thoughts.

For this reason, having multiple task goals places greater demands on the frontoparietal control and dorsal attention networks, which are limited in their capacities. At the same time, the ventral attention network is believed to support more automatic attention reorienting, which can include the capture of attention by distracting information. Importantly, when we multitask, the ventral attention network is more prone to be captured by competing streams of information, including information that is relevant to one task but irrelevant to--and thus disruptive of performance of--another task. Because on-task behavior emerges from interactions among the three brain networks, when we multitask, there are multiple competing sources of what constitutes relevant and irrelevant goals and information. This can cause interference and complex interactions between attention and control brain networks.

Switch Costs in Mind and Brain

One way we can examine the effects of multitasking on behavior and the demands it places on relevant brain networks is by analyzing "task switch costs." A switch costis a reduction in performance accuracy or speed that results from shifting between tasks. A rich body of research in psychological science has documented that the behavioral costs of task switching are typically unavoidable: individuals almost always take longer to complete a task and do so with more errors when switching between tasks than when they stay with one task. Neuroimaging work from our lab and others has helped to highlight the effects of task switching in the brain.

In one functional magnetic resonance imaging (fMRI) study, we had subjects classify stimuli on one of three dimensions (color, shape, or pattern). In terms of behavior, one finding was that subjects took longer to classify stimuli in switch trials (i.e., where the task had changed from the previous trial) compared to stay trials (i.e., where the task stayed the same). In terms of the brain, we found that frontoparietal regions--including those of the frontoparietal control and dorsal attention networks--were more responsive during switch than stay trials. In fact, consistent with the view that multitasking creates heightened neurocognitive demands, the strength of task representation in the control network was greater when subjects switched to a new task than when they stayed with the same task. This means that when we switch from one task to another, it requires more neural processing because we have to bring back to mind the new task's representation and then use it to allocate attention to information that is relevant to perform the new task. As a consequence, when we switch between tasks, we lose the benefits of automaticity and efficiency that come from staying focused on a single task.

Studies from other labs have reached similar conclusions. One fMRI studyexamined the effects of switching between tasks: subjects performed a single task repeatedly or two tasks intermixed in a block of trials. Response times were slower during task-switch blocks, and brain patterns reflected this effect. Nodes of the frontoparietal control network and dorsal attention network were more active during switch blocks, revealing increased neurocognitive demands associated with switching. From developmental studies, we have learned that older individuals often exhibit reduced abilities to selectively attend to and engage cognitive control in support of goal-directed behavior. Age-related fMRI studies provide initial hints as to what neural changes make multitasking (or task switching) particularly challenging for older adults. In one, older adults' diminished multitasking ability was associated with reduced connectivity between brain networks of attention, control, and memory, compared to young adults.

Psychological science and neuroscience indicate that our minds are taxed by multitasking. When we attempt it, we must engage in task switching, placing increased demands on neurocognitive systems that support control and sustained attention. While engaging these systems can partially mitigate its behavioral costs, multitasking is not free--we pay a price in increased demands on these systems and some performance deficit typically occurs.

A Spotlight on Media Multitasking

With the explosion of digital media and the commodification of our attention (referred to as the " attention economy"), "media multitasking" has become ubiquitous. Have you ever opened your laptop to check your email or complete a work assignment, and put on Spotify or Netflix in the background? This kind of multitasking--engaging with or switching between multiple media streams--has attracted considerable interest given behavioral trends. We know that American youth spend an average of 7.5 hours a day with various media and at least 29 percent of that time involves media multitasking. Data from other countries show a similar pattern, and the phenomenon extends to adults.

In 2009, Cliff Nass's lab at Stanford developed what has become a widely used index--the Media Multitasking Inventory (MMI)--to quantify the extent to which an individual engages in this practice. The original MMI asked individuals to report their hours of media consumption for each of 12 different media categories (television, music, text messaging, and so forth), along with the extent to which when engaged with one medium they were also engaged with each of the others. Test-retest reliability of MMI is high over a week (r = .93) and moderate over a one-year period (r =.52), and shorter versions and different variants have been developed.

The MMI score from the Nass lab represents the mean number of media with which an individual multitasks during a typical consumption hour. A high MMI score means an individual engages in a lot of media multitasking (e.g., checking email while also perusing Facebook and watching Netflix), and a low score means he or she does not (e.g., checking email without any secondary media). In the 2009 study, heavier and lighter media multitaskers were asked to perform a set of cognitive tasks that place demands on attention, control, and memory. This study initiated a rapidly evolving literature that seeks to answer the fundamental question: does media multitasking in everyday life impact our minds and brains, affecting performance even when we are single tasking?

One might expect a heavier media multitasker (HMM) to perform better on other tasks that require multitasking. While this inference seems reasonable, empirical studies are pretty mixed: some suggest that HMMs perform worse-i.e., exhibit larger switch costs--in task switching paradigms than do lighter media multitaskers (LMMs), while in others HMMs apparently perform as well as or better than LMMs.

Another finding that has emerged is that HMMs sometimes perform worse than LMMs even when single tasking, and this is most consistently seen on tasks that require sustained attention, working memory, long-term memory, and various forms of impulsivity and inhibitory control. That is, even when performing only a single task that requires them to maintain top-down attention or to keep information active in working memory, HMMs do more poorly--whether their performance is assayed by accuracy rates, response times, or related metrics--than LMMs. Some evidence suggests that this relationship holds even when controlling for the total number of media consumption hours, which means that the total number of hours spent with media is less predictive than the extent to which multitasking with media occurs during those hours.

For example, one recent study from our lab measured media multitasking, working memory, and long-term memory in undergraduate students at Stanford University. In one part of the study, subjects were asked to keep simple visual objects in memory over a brief (one second) delay and to make a memory-dependent decision after the delay. This is a standard task to measure working memory capacity (that is, the amount of information that can be held active in mind). After this came a long-term memory test: the subjects were shown the same objects they had seen during the working memory task along with novel ones and had to indicate which were seen earlier and which were new. We found that heavier media multitasking (as measured by the MMI) was associated with worse performance on both the working memory and long-term memory tasks. This was the case whether using an extreme groups approach (comparing the top 25 percent of individuals who were HMMs to the bottom 25 percent as LMMs) or a continuous approach (using data from all subjects, including light, intermediate, and heavy media multitaskers).

These results add to a growing body of work documenting the relationship between media multitasking and cognitive operations linked to sustained attention and working memory. To build on our prior work, one approach we have recently taken is to measure attention lapses at the trial and subject level while individuals of varying media multitasking habits perform single cognitive tasks. By trial level, we mean moment-to-moment (i.e., state-level) attention lapses during a task that could help predict why an individual performed well at one moment and less well at another; by subject level, we mean individual (i.e., trait-level) differences in attention lapsing that could help to predict differences in task performance across people. Previous work has shown that fluctuations in pupil diameter from eye tracking data and fluctuations in alpha and theta oscillatory power from electroencephalographic (EEG) data reliably track attention lapses during various single tasks.

In this ongoing project, we are measuring moment-to-moment changes in pupillary response and alpha and theta oscillatory power to quantify attention lapsing, with the aim of testing the relationship between lapsing, task performance, and MMI status. We are also learning more about which indices of sustained attention may relate to MMI status. (Here, we should note that while we and others have identified sustained attention as a potential mechanism behind performance differences between HMMs and LMMs, not all studies come to this conclusion. As we review in a recent publication, the discrepancy between studies could be due to differences in: (a) multitasking and performance measurement methodology, (b) demographics of the measured samples, (c) statistical power, and/or (d) analytic approach, among other reasons.)

There are a number of potential practical consequences of media multitasking in everyday life, one being academic outcomes. While we have primarily examined the relationship of MMI status and single tasking and multitasking performance in the lab, new translational studies offer a complementary perspective. For example, recent work has shown that students learn less when texting or using social media while attending lectures. Reading proficiency and homework accuracy have also been shown to decrease as individuals multitask with instant messaging and various computer programs.

In modern society, we are often pulled in different directions by competing sources of information, and how we react can affect our performance and quality of life, as highlighted by work on the "attention economy." A take-away from this body of research is that individuals should be thoughtful about the degree to which they engage in media and other forms of multitasking. As consumers of knowledge (and media), we should be cognizant of the potential relationships between task-switching, brain, and behavior, whether we are trying to complete a single task or to multitask. When we multitask, the context may matter. For example, recent data suggest that there are certain domains--like creative problem solving --that may benefit from task switching by reducing fixation on a problem. Weighing the costs and benefits of multitasking is important.

Critical challenges for the future include addressing the central question of causality, as well as determining the brain mechanisms that may underlie relationships between levels of media multitasking and cognition. For example, do pre-existing trait differences in attention or impulsivity lead to differences in media use and multitasking, or do media use and multitasking increase inattention and impulsivity? Are there tasks where heavier media multitaskers might outperform lighter media multitaskers? How do brain networks of attention and control differ between heavier and lighter media multitaskers, and how do these neural profiles relate to task performance? These are questions that researchers are just beginning to tackle--in the lab and in the field--highlighting how psychological science and neuroscience are living and breathing disciplines whose discoveries can inform fundamental issues faced by modern society.

Footnotes

Kevin P. Madore, Ph.D., is a Postdoctoral Fellow in psychology and neuroscience at Stanford University. He is interested in neurocognitive interactions among cognitive control, attention, and memory in young and older adults, as well as functions of memory like imagination and creativity. He uses a combination of behavioral, eye tracking, and neural methods, and focuses on state- and trait-level effects. He received his Ph.D. at Harvard University and his Bachelor of Arts degree at Middlebury College.

Anthony D. Wagner, Ph.D., is a Lucie Stern Professor in the social sciences and chair of the Department of Psychology at Stanford University. He is on the board of Stanford's Center for Cognitive and Neurobiological Imaging, is a contributor to the Stanford Program in Neuroscience and Society, and is a faculty affiliate of the Wu Tsai Neurosciences Institute, Symbolic Systems Program, Human Biology Program, and Stanford Center on Longevity. His research focuses on the psychology and neurobiology of learning, memory, and executive function in young and older adults. In addition to his basic science and translational work, he examines the implications of neuroscience for law (including as a member of the MacArthur Foundation's Research Network on Law and Neuroscience) and the relationship between multitasking and cognition. He is a reviewing editor at Cerebral Cortex and serves on several other editorial boards. He received his Ph.D. at Stanford University and his Bachelor of Arts degree at the University of California, Los Angeles.

Articles from Cerebrum: the Dana Forum on Brain Science are provided here courtesy of Dana Foundation

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What happens to your brain when you multitask? Does your brain slow down? Do you feel increased levels of stress? Why are some people better at it than others? Our authors supply the answers to some of these questions and provide the latest on what happens ...

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Low-Stakes Quizzes Improve Learning and Reduce Overconfidence in College Students

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Low-Stakes Quizzes Improve Learning and Reduce Overconfidence in College Students

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"One simple and effective method of improving long-term learning is retrieval practice: the process of attempting to retrieve information at some point after it was initially learned. For instance, teachers can initiate retrieval practice in the classroom by asking students questions about the material, giving pop quizzes, or reviewing for an exam. Students can also initiate the process while studying by attempting to answer review questions in a textbook, creating flashcards, or having classmates test them."

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How Common Is Belief in the Learning Styles Neuromyth, and Does It Matter? A Pragmatic Systematic Review

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How Common Is Belief in the Learning Styles Neuromyth, and Does It Matter? A Pragmatic Systematic Review

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"A number of reviews, going back to 2004, have concluded that there is currently no empirical evidence that this 'matching instruction' improves learning, and it could potentially cause harm."

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Swansea University Medical School, Swansea University, Swansea, United Kingdom

A commonly cited use of Learning Styles theory is to use information from self-report questionnaires to assign learners into one or more of a handful of supposed styles (e.g., Visual, Auditory, Converger) and then design teaching materials that match the supposed styles of individual students. A number of reviews, going back to 2004, have concluded that there is currently no empirical evidence that this "matching instruction" improves learning, and it could potentially cause harm. Despite this lack of evidence, survey research and media coverage suggest that belief in this use of Learning Styles theory is high amongst educators. However, it is not clear whether this is a global pattern, or whether belief in Learning Styles is declining as a result of the publicity surrounding the lack of evidence to support it. It is also not clear whether this belief translates into action. Here we undertake a systematic review of research into belief in, and use of, Learning Styles amongst educators. We identified 37 studies representing 15,405 educators from 18 countries around the world, spanning 2009 to early 2020. Self-reported belief in matching instruction to Learning Styles was high, with a weighted percentage of 89.1%, ranging from 58 to 97.6%. There was no evidence that this belief has declined in recent years, for example 95.4% of trainee (pre-service) teachers agreed that matching instruction to Learning Styles is effective. Self-reported use, or planned use, of matching instruction to Learning Styles was similarly high. There was evidence of effectiveness for educational interventions aimed at helping educators understand the lack of evidence for matching in learning styles, with self-reported belief dropping by an average of 37% following such interventions. From a pragmatic perspective, the concerning implications of these results are moderated by a number of methodological aspects of the reported studies. Most used convenience sampling with small samples and did not report critical measures of study quality. It was unclear whether participants fully understood that they were specifically being asked about the matching of instruction to Learning Styles, or whether the questions asked could be interpreted as referring to a broader interpretation of the theory. These findings suggest that the concern expressed about belief in Learning Styles may not be fully supported by current evidence, and highlight the need to undertake further research on the objective use of matching instruction to specific Learning Styles.

Introduction

For decades, educators have been advised to match their teaching to the supposed Learning styles of students ( Hyman and Rosoff, 1984). There are now over 70 different Learning Styles classification systems ( Coffield et al., 2004). They are largely questionnaire-based; students are asked to self-report their preferences for different approaches to learning and other activities and are then assigned one or more Learning Styles. The VARK classification is perhaps the most well-known ( Newton, 2015; Papadatou-Pastou et al., 2020), which categorizes individuals as one or more of Visual, Auditory, Read-Write and Kinesthetic learners. Other common Learning Styles classifications in the literature include those by Kolb, Honey and Mumford, Felder, and Dunn and Dunn ( Coffield et al., 2004; Newton, 2015).

In the mid-2000s two substantial reviews of the literature concluded that there was currently no evidence to support the idea that the matching of instructional methods to the supposed Learning Styles of individual students improved their learning ( Coffield et al., 2004; Pashler et al., 2008). Subsequent reviews have reached the same conclusion ( Cuevas, 2015; Aslaksen and Lorås, 2018) and there have been numerous, carefully controlled attempts to test this "matching" hypothesis (e.g., ( Krätzig and Arbuthnott, 2006; Massa and Mayer, 2006; Rogowsky et al., 2015, 2020; Aslaksen and Lorås, 2019). The identification of supposed student Learning Style does not appear to influence the way in which students choose to study ( Husmann and O'Loughlin, 2018), and does not correlate with their stated preferences for different teaching methods ( Lopa et al., 2015).

Despite this lack of evidence, a number of studies suggest that many educators believe that matching instruction to Learning Style(s) is effective. One of the first studies to test this belief was undertaken in 2009 and looked at various statements about the brain and nervous system which are widespread but which are not supported by research evidence, for example the idea what we only use 10% of our brain, or that we are born with all the brain cells that we will ever have. The study described such statements as "neuromyths" and showed that belief in them was high, including belief in matching of instruction to Learning Styles which was reported by 82% of a sample of trainee teachers in the United Kingdom ( Howard-Jones et al., 2009). A number of similar studies have been conducted since, and have reached the same conclusion, with belief in Learning Styles reaching as high as 97.6% in a study of preservice teachers in Turkey ( Dündar and Gündüz, 2016).

This apparent widespread belief in an ineffective teaching method has caused concern amongst the education community. Part of the concern arises from a perception that the use of Learning Styles is actually harmful ( Pashler et al., 2008; Riener and Willingham, 2010; Dekker et al., 2012; Rohrer and Pashler, 2012; Dandy and Bendersky, 2014; Willingham et al., 2015). The proposed harms include concerns that learners will be pigeonholed or demotivated by being allocated into a Learning Style. For example, a student who is categorized as an "auditory learner" may conclude that there is no point in pursing studies, or a career, in visual subjects such as art, or written subjects such as journalism and so be demotivated during those classes. They might also conclude that they will be more successful in auditory subjects such as music, and thus inappropriately motivated by unrealistic expectations of success and become demotivated if that success does not materalise. It is worth noting however that many advocates of Learning Styles propose that it may be motivating for individual learners to know their supposed style ( Coffield et al., 2004). Another concern is that to try and match instruction to Learning Styles risks wasting resources and effort on an ineffective method. Educators are motivated to try and do the best for their learners, and a logical extension of the matching hypothesis is that educators would need to try and generate 4 or more versions of their teaching materials and activities, to match the different styles identified in whatever classification they have used. Additional concerns are that the continued belief in Learning Styles undermines the credibility of educators and education research, and creates unwarranted and unrealistic expectations of educators ( Newton and Miah, 2017). These unrealistic expectations could also manifest when students do not achieve the academic grades that they expect, or do not enjoy, or engage with, their learning; if students are not taught in a way that matches their supposed Learning Style, then they may attribute these negative experiences to a lack of matching and be further demotivated for future study. These concerns, and controversy, have also generated publicity in the media, both the mainstream media and in publications focused on educators ( Pullmann, 2017; Strauss, 2017; Brueck, 2018).

The lack of evidence to support the matching hypothesis is now acknowledged by some proponents of Learning Styles theory. For example Richard Felder states in a 2020 opinion piece

" As the critics of learning styles correctly claim, the meshing hypothesis (matching instruction to students' learning styles maximizes learning) has no rigorous research support, but the existence and utility of learning styles does not rest on that hypothesis and most proponents of learning styles reject it." ( Felder, 2020)

and

" I now think of learning styles simply as common patterns of student preferences for different approaches to instruction, with certain attributes - behaviors, attitudes, strengths, and weaknesses - being associated with each preference". ( Felder, 2020)

This specific distinction between the matching/meshing hypothesis, and the existence of individual preferences, is at the heart of many studies which have examined belief in the matching hypothesis. Many studies ask about both preferences and matching. These are very different concepts, but the wording of the questions asked about them is very similar. Here for example is the original wording of the questions used in Howard-Jones et al. (2009), which has been used in many studies since. Participants are asked to rate their agreement with the statements that;

" Individuals learn better when they receive information in their preferred learning style (e.g., auditory, visual, kinesthetic)" ( Matching question).

and, separately,

" Individual learners show preferences for the mode in which they receive information (e.g., visual, auditory, kinesthetic)" ( Preferences question).

This potential overlap in concepts is reflected in studies which have examined what educators understand by the term Learning Styles. A 2020 qualitative study investigated this in detail and found a range of different interpretations of the term Learning Styles. Although the VAK/VARK classification system was the most commonly recognized classification, many educators incorrectly conflated it with other theories, such as Howard Gardeners theory of Multiple Intelligences, and learning theories such as cognitivism. There was also a large diversity in the ways in which educators attempted to account for the use of Learning Styles in their teaching practice. Many educators responded by including a diversity of approaches within their teaching, but not necessarily mapped onto specific Learning Styles instrument or with instruction specific to individuals. For example using a wide variety of audiovisual modalities, or a diversity of active approaches to learning ( Papadatou-Pastou et al., 2020). An earlier study reported that participants incorrectly used the term "Learning Styles" interchangeably with "Universal Design for Learning," and other strategies that take into account individual differences (differentiation) ( Ruhaak and Cook, 2018). This complexity is reflected in teacher-training textbooks, which commonly refer to Learning Styles but in a variety of ways, including student motivation and preferences for learning ( Wininger et al., 2019). There is also a related misunderstanding about Learning Styles theory; the absence of evidence for a matching hypothesis does not mean that students should all be taught the same way, or that they do not have preferences for how they learn. Attempts to refute the matching hypothesis have been incorrectly interpreted in this way ( Newton and Miah, 2017).

Thus, one interpretation of the current literature and surrounding media is that, concern has arisen due to widespread belief in the efficacy of an ineffective and potentially harmful teaching technique, but the participants in studies which report on this widespread belief do not clearly understand what they are being asked, or what the intended consequences are if they disagree with what they are asked.

One set of questions to be addressed in this review then is whether the aforementioned concern is fully justified, and whether this potential confusion is reflected in the data. We examine this by using a systematic review approach to take a broader look at trends and patterns in a larger dataset. The evidence showing a lack of evidence for matching instruction to Learning Styles has been available since 2004. It would be reasonable then to expect that belief in this method would have declined since then, particularly if it is harmful. A related question is whether educators actually use Learning Styles; to generate multiple versions of teaching materials and activities would require considerable additional effort for no apparent benefit, which should also hasten the decline of Learning Styles.

Research Questions

1. What percentage of educators believe in the matching of instruction to Learning Styles?

2. What percentage of educators enact, or plan to enact the matching of instruction to Learning Styles?

3. Has belief in matching instruction to Learning Styles decreased over time?

4. Do evidence-based interventions reduce belief in matching instruction to Learning Styles?

5. Do studies present clear evidence that participants understand the difference between (a) matching instruction to Learning Styles and (b) preferences exhibited by learners for the ways in which they receive information?

Methods

Eligibility Criteria, Information Sources, and Search Strategy

Education research is often published in journals that are outside the immediate field of education, but instead are linked to the subject being learned. Therefore, we used EBSCO to search the following databases: CINAHL Plus with Full Text; eBook Collection (EBSCOhost); Library, Information Science & Technology Abstracts; MEDLINE; APA PsycArticles; APA PsycINFO; Regional Business News; SPORTDiscus with Full Text; Teacher Reference Center; MathSciNet via EBSCOhost; MLA Directory of Periodicals; MLA International Bibliography. We also searched PubMed and the Education Research database ERIC.

The following search terms were used: "belief in learning styles"; "believe in learning styles"; "believed in learning styles"; "Individuals learn better when they receive information in their preferred learning style" (this is the survey question used in the original Howard-Jones paper ( Howard-Jones et al., 2009). Neuromyth*; "learning styles" AND myth AND survey or questionnaire. We used advanced search settings for all sources to apply related words and to ensure that the searches looked for the terms within the full text of the articles. No date restriction was applied to the searches and so the results included items up to and including April 2020.

This returned 1,153 items. Exclusion of duplicates left 838 items. These were then screened according to the inclusion criteria (below). Screening articles on the basis of their titles identified 85 eligible items. The abstracts of these were then evaluated which resulted in 46 items for full-text screening. We also used Google Scholar to search for the same terms. Google Scholar provides better inclusion of non-journal research including of gray literature ( Haddaway et al., 2015) and unpublished theses that are hosted on servers outside the normal databases ( Jamali and Nabavi, 2015). For example, when searching for the specific survey item used in the original Howard-Jones paper ( Howard-Jones et al., 2009) and in many studies subsequently; "Individuals learn better when they receive information in their preferred learning style." This search returned zero results on ERIC and four result on PsychINFO, but returned 107 results on Google Scholar, most of which were relevant. However, all Google Scholar results had to hand screened in real-time since Google Scholar does not have the same functionality as the databases described above; it includes multiple versions of the same papers, and the search interface is limited, making it difficult to accurately quantify and report search results ( Boeker et al., 2013).

Study Selection

To be included in the review a study had to meet the following criteria;

* Survey educators about their belief in the matching of instruction to one or more of the Learning Styles classifications identified in aforementioned reviews ( Coffield et al., 2004; Pashler et al., 2008) and/or educators use of that matching in their teaching. This included pre-service or trainee teachers (individuals studying toward a teaching qualification).

* Report sufficient data to allow calculation of the number and percentage of respondents stating a belief that individuals learn better when they receive information in their preferred learning style (or use/plan to use Learning Styles theory in this way).

Exclusion criteria included the following

* Surveys of participant groups that were not educators or trainee educators.

* Only survey belief in individual learning preferences (i.e., rather than matching instruction).

* Survey other opinions about Learning Styles, for example whether they explain differences in academic abilities (e.g., Bellert and Graham, 2013).

* Survey belief in personalizing learning to suit preferences or other characteristics not included in the Learning Styles literature (e.g., prior educational achievement, "deep, surface or strategic learners."

Some studies were not explicitly clear that they surveyed belief in matching instruction, but used related non-specific concepts such as the "existence of Learning Styles." These were excluded unless additional information was available to confirm that the studies specifically surveyed belief in matching instruction to Learning Styles. For example ( Grospietsch and Mayer, 2018) reported surveying belief in the existence of Learning Styles. However, the content of this paper discussed knowledge acquisition in the context of matching, and stated that the research instruments was derived from Dekker et al. (2012), and had been used in an additional paper by the same authors ( Grospietsch and Mayer, 2019), while a follow-up paper from the same authors described both these earlier papers as surveying belief in matching instruction to Learning Styles ( Grospietsch and Mayer, 2020). These two survey studies were therefore included. Another study ( Canbulat and Kiriktas, 2017) was not clear and no additional information was available. Two emails were sent to the corresponding author with a request for clarity, but no response was received.

Application of the inclusion criteria resulted in 33 studies being included, containing a total of 37 samples. We then went back to Google Scholar to search within those articles which cited the 33 included studies. No further studies were identified which met the inclusion criteria.

Data Collection Process

Data were independently extracted from every paper by two authors working separately (PN + AS). Extracted data were then compared and any discrepancies resolved through discussion.

Data Items

The following metrics were collected where available (all data are shown in Appendix 1):

* The year the study was published

* Year that data were collected (where stated, and if different from publication date. If a range was stated, then the year which occupied the majority of the range was taken (e.g., Aug 2014-April 2015 was recorded as 2014).

* Country where the research was undertaken

* Publication type (peer reviewed journal, thesis, gray literature)

* Population type (e.g., academics in HE, teachers, etc.)

* Whether or not funding was received and if so where from

* Whether or not a Conflict of Interest was reported/detected

* Target population size

* Sample size

* "N" (completed returns)

* Average teaching experience of participant group

* Percentage and number of participants who stated agreement with a question regarding belief in the matching of instruction to Learning Styles, and the text of the specific question asked

* Percentage and number of participants who stated agreement with a question regarding belief that learners express preferences for how they receive information, and the text of the specific question asked

* The percentage and number of participants who stated that they did, or would, use matching to instruction in their teaching, and the text of the specific question asked

* The percentage and number of participants who stated agreement with a question regarding belief in the matching of instruction to Learning Styles after any intervention aimed at helping participants understand the lack of evidence for matching instruction to Learning Styles

Summary Measures and Synthesis of Results

Most measures are simple percentages of participants who agreed, or not, with questionnaire statements. Summary measures are then the average of these. In order to account for unequal sample size, simple weighted percentages were calculated; percentages were converted to raw numbers using the stated "N" for an individual sample. The sum of these raw numbers from each study was then divided by the sum of "N" from each study and converted to a percentage. Percentages from individual studies were used as individual data points in groups for subsequent statistical analysis, for example to compare the percentage of participants who believed in matching instruction to the percentage who actually used Learning Styles in this way.

Risk of Bias Within and Across Studies

Bias is defined as anything which leads a review to "over-estimate or under-estimate the true intervention effect" ( Boutron et al., 2019). In this case an "intervention effect" would be belief in, or use of, Learning Styles either before or after any intervention, or belief in a preference for receiving information in different ways.

Many concerns regarding bias are unlikely to apply here. For example, publication bias, wherein results are less likely to be reported if they are not statistically significant. Most of the data reported in the studies under consideration here are not subject to tests of significance, so this is less of a concern.

* Sampling Method. Each study was classified into one of the following categories. Categories are drawn from the literature ( Kelley et al., 2003) and the studies themselves.

◦ Convenience sampling. The survey was distributed to all individuals within a specified population, and data were analyzed from those individuals who voluntarily completed the survey.

◦ Snowball sampling. Participants from a convenience sample were asked to then invite further participants to complete the survey.

◦ Unclassifiable. Insufficient information was provided to allow determination of the sampling method

◦ (no other sampling approaches were used by the included studies)

* Validity Measures

◦ Neutral Invitation. Were participants invited to the study using neutral language. Neutrality in this case was defined as not demonstrating support for, or criticism of, Learning Styles in a way that could influence the response of a participant. An example of a neutral invitation is Dekker et al. (2012) " The research was presented as a study of how teachers think about the brain and its influence on learning. The term neuromyth was not mentioned in the information for teachers."

◦ Learning Styles vs. styles of learning. Was sufficient information made available to participants for them to be clear that they were being asked about Learning Styles rather than styles of learning, or preferences ( Papadatou-Pastou et al., 2020). For example, was it explained that, in order to identify a Learning Style, a questionnaire needs to be administered which then results in learners being allocated to one or more styles, with named examples (e.g., Newton and Miah, 2017).

◦ Matching Instruction. If yes to above, was it also made clear that, according to the matching hypothesis, educators are supposed to tailor instruction to individual learning styles.

Additional Analyses

The following additional analyses were pre-specified in line with our initial research questions.

Has Belief in Matching Instruction to Learning Styles Decreased Over Time?

The lack of evidence to support matching instruction to Learning Styles has been established since the mid-2000s and has been the subject of substantial publicity. We might therefore hypothesize that belief in matching instruction has decreased over time, for example due to the effects of the publicity, and/or from a revision of teacher-training programmes to reflect this evidence. Three different analyses were conducted to test for evidence of a decrease.

1. A Spearman Rank Correlation test was conducted to test for a correlation between the year that the study was undertaken and the percentage of participants who reported a belief in matching instruction to learning styles. A significant negative correlation would indicate a decrease over time.

2. Belief in matching instruction to Learning Styles was compared in trainee teachers vs. practicing teachers. If belief in Learning Styles was declining then we would expect to see lower rates of belief in trainee teachers. Two samples ( Tardif et al., 2015; van Dijk and Lane, 2018) contained a mix of trainee and qualified teachers and were excluded from this analysis. The samples of teachers in Dekker et al. (2012) and Macdonald et al. (2017) both contained 94% practicing teachers and 6% trainee teachers, and so the samples were counted as practicing teachers for the purpose of this analysis.

3. A Spearman Rank Correlation test was conducted to test for a correlation between the average teaching experience of study participants and the percentage of participants who reported a belief in matching instruction to Learning Styles. If belief in matching instruction to learning styles is decreasing then we might expect to see a negative correlation.

Is There a Difference Between Belief in Learning Styles and Use of Learning Styles

The weighted percentage for each of these was calculated, and the two groups of responses were also compared.

Question Validity Analysis

In many of the studies here, participants were asked about both "preferences for learning" and "matching instruction to Learning Styles." As described in the introduction, the wording for both questions was similar. If there was confusion about the difference between these two statements, then we would expect the pattern of response to them to be broadly similar. To test for this, we calculated a difference score for each study by subtracting the percentage of participants who believed in matching instruction to Learning Styles from the percentage who agreed that individuals have preferences for how they learn. We then conducted a one-tailed t-test to determine whether the distribution of these scores was significantly different from zero. We also compared both groups of responses.

Analysis

All datasets were checked for normal distribution before analysis using a Kolmogorov-Smirnov test. Non-parametric tests were used where datasets failed this test. Individual tests are described in the results section.

Results

89.1% of Participants Believe in Matching Instruction to Learning Styles

34/37 samples reported the percentage of participants who stated agreement with an incorrect statement that individuals learn better when they receive information in their preferred learning style. The simple average of these 34 data points is 86.2%. To calculate a weighted percentage, these percentages were converted to raw numbers using the stated "N." The sum of these raw numbers was then divided by the sum of "N" from the 34 samples to create a percentage. This calculation returned a figure of 89.1%. A distribution of the individual studies is shown in Figure 1.

Figure 1. The percentage of participants who stated agreement that individuals learn better when they receive information in their preferred Learning Style. Individual studies are shown with the name of the first author and the year the study was undertaken. Data are plotted as ±95% CI. Bubble size is proportional to the Log10 of the sample size.

No Evidence of a Decrease in Belief Over Time

As described in the methods we undertook three separate analyses to test for evidence that belief in Learning Styles has decreased over time. (1) A Spearman Rank correlation analysis was conducted to test for a relationship between the year a study was conducted and the percentage who reported that they believed in matching instruction to Learning Styles. No significant relationship was found ( r = −0.290, P = 0.102). (2) Belief in matching instruction to Learning Styles was compared in samples of qualified teachers ( N = 16) vs. pre-service teachers ( N = 12) using a Mann-Whitney U test. No significant difference was found ( Figure 2). A Mann Whitney U test returned a P value of 0.529 (U = 82). When calculating the weighted percentage from each group, belief in matching was 95.4% for pre-service teachers and 87.8% for qualified teachers. The weighted percentage for participants from Higher Education was 63.6%, although this was not analyzed statistically since these data were calculated from only three studies and these were different to the others in additional ways (see Discussion). (3) A Spearman Rank correlation analysis was conducted to test for a relationship between the mean years of experience reported by a participant group (qualified teachers) and the percentage who reported that the believed in matching instruction to Learning Styles. No significant relationship was found ( r = −0.158, P = 0.642).

Figure 2. No difference between the percentage of Qualified Teachers vs. Pre-Service Teachers who believe in the efficacy of matching instruction to Learning Styles. The percentage of educators who agreed with each statement was compared by Mann-Whitney U test. P = 0.529.

Effect of Interventions

Four studies utilized some form of training for participants, to explain the lack of current evidence for matching instruction to Learning Styles. A pre-post test analysis was used in these studies to evaluate participants belief in the efficacy of matching instruction to Learning Styles both before and after the training. Calculating a weighted percentage revealed that, in these four studies, belief went from 78.4 to 37.1%. The effect size for this intervention effect was large (Cohens d = 3.6). Comparing these four studies using a paired t-test revealed that the difference between pre and post was significant ( P = 0.012). Results from the individual studies are shown in Figure 3.

Figure 3. Interventions which explain the lack of evidence to support the efficacy of matching instruction to Learning Styles are associated with a drop in the percentage of participants who report agreeing that matching is effective. Each of the four studies used a pre-post design to measure self-reported belief. The weighted percentage dropped from 78.4 to 37.1%.

Use of Learning Styles vs. Belief

Seven studies measured self-report of use, or planned use, of matching instruction to Learning Styles. Calculating the weighted average revealed that 79.7% of participants said they used, or intended to use, the matching of instruction to Learning Styles. This was compared to the percentage who reported that they believed in the efficacy of matching instruction. A Mann-Whitney U test was used since four of the seven studies did not measure belief in matching to instruction and so a paired test was not possible. No significant difference was found between the percentage of participants who reported believing that matching instruction to Learning Styles is effective (89.1%), and the percentage who used, or planned to use, it as a teaching method (79.7%) ( P = 0.146, U = 76.5). Data are shown in Figure 4.

Figure 4. No difference between the percentage of participants who report believing in the efficacy of matching instruction to Learning Styles, and the percentage who used, or intended to use, Learning Styles in this way. The pooled weighted percentage was 89.1 vs. 79.7%. P = 0.146 by Mann-Whitney U test.

No Difference in Belief in Preferences vs. Belief in Matching Instruction to Learning Styles

As described in the introduction, many studies compared belief in matching instruction to Learning Styles (a "neuromyth") with a correct statement that individuals show preferences for the mode in which they receive information. Twenty-one studies questioned participants on both their belief in matching instruction to Learning Styles, and their belief that individual learners have preferences for the ways in which they receive information. A Wilcoxon matched-pairs test showed no significant difference between these two datasets ( P = 0.262, W = 57). A difference score was calculated by subtracting the percentage who believe in matching instruction from the percentage who believe that learners show preferences. The mean of these scores was 2.66, with a Standard Deviation of 8.97. A one sample t-test showed that the distribution of these scores was not significantly different from zero ( P = 0.189). The distribution of these scores is shown in Figure 5 and reveals many negative scores, i.e., where belief in matching instruction to Learning Styles is higher than a belief that individuals have preferences for how they receive information.

Figure 5. No difference between belief in Learning Styles and Learning Preferences. (A) The percentage of participants who report believing that individuals have preferences for how the receive information, and the percentage who report believing that individuals learn better when receiving information in their preferred Learning Style. (B) The difference between these two measures, calculated for individual samples. A negative score means that fewer participants believed that students have preferences for how they received information compared to the percentage who believed that matching instruction to Learning Styles is effective.

Risk of Bias and Validity Measures

A summary table of the individual studies is shown in Table 1. (The full dataset is available in Appendix 1).

Table 1. Characteristics of included studies.

Of the 34 samples which measured belief in matching instruction to Learning Styles, 30 of them used the same question as used in Howard-Jones et al. (2009) (see Introduction). The four which used different questions were "Does Teaching to a Student's Learning Style Enhance Learning?" ( Dandy and Bendersky, 2014), "Students learn best when taught in a manner consistent with their learning styles" ( Kilpatrick, 2012), "How much do you agree with the thesis that there are different learning styles (e.g., auditory, visual or kinesthetic) that enable more effective learning?" ( Menz et al., 2020) and "A pedagogical approach based on such a distinction favors learning" (participants had been previously been asked to rate their agreement with the statement "Some individuals are visual, others are auditory") ( Tardif et al., 2015).

Sampling

Thirty of the 37 samples included used convenience sampling. Three of the studies used snowballing from convenience sampling, while the remaining 4 were unclassifiable; these were all from one study whose participants were recruited " at various events related to education (e.g., book fair, pedagogy training sessions, etc.), by word of mouth, and via email invitations to databases of people who had previously enquired about information/courses on neuroscience and education" ( Gleichgerrcht et al., 2015). Thus, no studies used a rigorous, representative, random sample and so no further analysis was undertaken on the basis of sampling method. Some studies considered representativeness in their methodology, for example Dekker et al. (2012) reported that the local schools they approached " could be considered a random selection of schools in the UK and NL" but the participants were then " Teachers who were interested in this topic and chose to participate." No information is given about the size of the population or the number of individuals to whom the survey was sent, and no demographic characteristics are given regarding the population.

Response Rate

Only five samples reported the size of the population from which the sample was drawn, and so no meaningful analysis of response rate can be drawn across the 37 samples. In one case ( Betts et al., 2019) the inability to calculate a response rate was due to our design rather than the study from which the data were extracted; Betts et al. (2019) reported distributing their survey to a Listserv of 65,780, but the respondents included many non-educators whose data were not relevant for our research question. It is perhaps worth noting however that their total final participant number was 929 and so their total response rate across all participant groups was 1.4%

Neutral Invitation

Nine of the 37 studies presented evidence of using a neutral invitation. None of the remaining studies provided evidence of a biased invitation; the information was simply not provided.

Briefing on Learning Styles and Matching

Two of the 37 studies reported giving participants additional information regarding Learning Styles, sufficient (in our view) for participants to be clear that they were being asked specifically about Learning Styles as defined by Coffield et al., and the matching on instruction to Learning Styles.

Discussion

We find that 89.1% of 15,045 educators, surveyed from 2009 through to early 2020, self-reported a belief that individuals learn better when they receive information in their preferred Learning Style. In every study analyzed, the majority of educators reported believing in the efficacy of this matching, reaching as high as 97.6% in one study by Dundar and colleagues, which was also the largest study in our analysis, accounting for 19% of the total sample ( Dündar and Gündüz, 2016).

Perhaps the most concerning finding from our analysis is that there is no evidence that this belief is decreasing, despite research going back to 2004 which demonstrates that such an approach is ineffective and potentially harmful. We conducted three separate analyses to test for evidence of a decline but found none, in fact the total percentage of pre-service teachers who believe in Learning Styles (95.4%) was higher than the percentage of qualified teachers (87.8%). This finding suggests that belief in matching instruction to Learning Styles is acquired before, or during, teacher training. Tentative evidence in support for this is a preliminary indication that belief in Learning Styles may be lower in educators from Higher Education, where teacher training is less formal and not always compulsory. In addition, Van Dijk and Lane report that overall belief in neuromyths is lower in HE although they do not report this breakdown for their data on Learning Styles ( van Dijk and Lane, 2018). However, the studies from Higher Education are small, and two of them are also studies where more information is provided to participants about Learning Styles (see below).

From our pragmatic perspective, there are a number of issues to consider when determining whether these findings should be a cause for alarm, and what to do about them.

The data analyzed here are mostly extracted from studies which assess teacher belief in a range of so-called neuromyths. These all use some version of the questionnaire developed by Howard-Jones and co-workers ( Howard-Jones et al., 2009). The value of surveying belief in neuromyths has been questioned, on the basis that, in a small sample of award-winning teachers, there did not appear to be any correlation between belief in neuromyths and receiving a teaching award ( Horvath et al., 2018). The Horvath study ultimately proposed that awareness of neuromyths is "irrelevant" to determining teacher effectiveness and played down concerns, expressed elsewhere in the field, that belief in neuromyths might be harmful to learners, or undermine the effectiveness of educators. We have only analyzed one element of the neuromyths questionnaire (Learning Styles), but we share some of the concerns expressed by Horvath and co-workers. The majority (30/34) of the samples analyzed here measured belief in Learning Styles using the original Howard-Jones/Dekker questionnaire. A benefit of having the same questions asked across multiple studies is that there is consistency in what is being measured. However, a problem is that any limitations with that instrument are amplified within the synthesis here. One potential limitation with the Howard-Jones question set is that the "matching" question is asked in many of the same surveys as a "belief" question, as shown in the introduction, potentially leading participants to conflate or confuse the two. Any issues may then be exacerbated by a lack of consistency in what participants understand by "matching instruction to Learning Styles"; this could affect all studies. The potential for multiple interpretations of these questions regarding Learning Styles is acknowledged by some authors (e.g., Morehead et al., 2016), and some studies report a lack of clarity regarding the specific meaning of Learning Styles and the matching hypothesis ( Ruhaak and Cook, 2018; Papadatou-Pastou et al., 2020). This lack of clarity is reflected also in the psychometric properties of Learning Styles instruments themselves, with many failing to meet basic standards of reliability and validity required for psychometric validation ( Coffield et al., 2004). In addition, we have previously founds that participants, when advised against matching instruction to Learning Styles, may conclude that this means educators should eliminate any consideration of individual preferences or variety in teaching methods ( Newton and Miah, 2017).

Here we found no significant differences between participant responses to the question regarding belief in matching instruction vs. the question about individual preferences, with almost half the studies analyzed actually reporting a higher percentage of participants who believed in matching instruction when compared to belief that individuals have preferences for how they receive information. This is concerning from a basic methodological perspective. The question is normally thus; " Individual learners show preferences for the mode in which they receive information (e.g., visual, auditory, kinesthetic)." In any sample of learners, some individuals are going to express preferences. It may not be all learners, and those preferences may not be stable for all learners, and the question does not encompass all preferences, but the question, as asked, cannot be anything other than true.

More relevant for our research questions is the apparent evidence of a lack of clarity within the research instrument; it may not be clear to study participants what the matching hypothesis is and so it is difficult to conclude that the results truly represent belief in matching instruction to Learning Styles. This finding is tentatively supported by our analysis which shows that, in the two studies which give participants additional instructions and guidance to help them understand the matching hypothesis, belief in matching instruction to Learning Styles is much lower, a weighted average of 63.5% ( Dandy and Bendersky, 2014; Newton and Miah, 2017). However, these are both small studies, and both are conducted in Higher Education rather than school teaching, so the difference may be explained by other factors, for example the amount and nature of teacher-training given to educators in Higher Education when compared to school-teaching. It would be informative to conduct further studies in which more detail was provided to participants about Learning Styles, before they are asked whether or not they believed matching instruction to Learning Styles is effective.

However, even if we conclude that the findings represent, in part, a lack of clarity over the specific meaning of "matching instruction to Learning Styles," this might itself still be a cause for concern. The theory is very common in teacher training and academic literature ( Newton, 2015; National Council on Teacher Quality, 2016; Wininger et al., 2019) and so we might hope that the meaning and use of it is clear to a majority of educators. An additional potential limitation is that the Howard-Jones question cites VARK as an example of Learning Styles, when there are over 70 different classifications. Thus we have almost no information about belief in other common classifications, such as those devised by Kolb, Honey and Mumford, Dunn and Dunn etc. ( Coffield et al., 2004).

79.7% of participants reported that they used, or planned to use, the approach of matching instruction to Learning Styles. This high percentage was surprising since our earlier work ( Newton and Miah, 2017) showed that only 33% of participants had used Learning Styles in the previous year. If Learning Styles are ineffective, wasteful of resources and even harmful, then we might predict that far fewer educators would actually use them. There are a number of caveats to the current results. There are only seven studies which report on this and all are small, accounting for <10% of the total sample. Most are not paired, i.e., they do not explicitly ask about belief in the efficacy of Learning Styles and then compare it to use of Learning Styles. The questions are often vague, broad and do not specifically represent an example of matching instruction to individual student Learning Styles as organized into one of the recognized classifications. For example "do you teach to accommodate those differences" (Learning Styles). Agreement with statements like these might reflect a belief that educators feel like they have to say they use them in order to respect any/all individual differences, rather than Learning Styles specifically. In addition this is still a self-report of a behavior, or planned behavior. It would be useful, in further work, to measure actual behavior; how many educators have actually designed distinct versions of educational resources, aligned to multiple specific individual student Learning Styles? This would appear to be a critical question when determining the impact of the Learning Styles neuromyth.

The studies give us little insight into why belief in Learning Styles persists. The theory is consistently promoted in teacher-training textbooks ( National Council on Teacher Quality, 2016) although there is some evidence that this is in decline ( Wininger et al., 2019). If educators are themselves screened using Learning Styles instruments as students at school, then it seems reasonable that they would then enter teacher-training with a view that the use of Learning Styles is a good thing, and so the cycle of belief would be self-perpetuating.

We have previously shown that the research literature generally paints a positive picture of the use of Learning Styles; a majority of papers which are "about" Learning Styles have been undertaken on the basis that matching instruction to Learning Styles is a good thing to do, regardless of the evidence ( Newton, 2015). Thus an educator who was unaware of, or skeptical of, the evidence might be influenced by this. Other areas of the literature reflect this idea. A 2005 meta-analysis published in the Journal of Educational Research attempted to test the effect of matching instruction to the Dunn and Dunn Learning Styles Model. The results were supposedly clear;

" results overwhelmingly supported the position that matching students' learning-style preferences with complementary instruction improved academic achievement" ( Lovelace, 2005).

An early attempt by Dunn and Dunn to promote the use of their Learning Styles classification was made on the basis that teachers would be less likely to be the subject of malpractice lawsuits if they could demonstrate that they had made every effort to identify the learning styles of their students ( Dunn et al., 1977). This is perhaps an extreme example, but reflective of a general sense that, by identifying a supposed learning style, educators may feel they are doing something useful to help their students.

A particular issue to consider from a pragmatic perspective is that of study quality. Many of the studies did not include key indicators of the quality of survey responses ( Kelley et al., 2003; Bennett et al., 2011). For example, none of the studies use a defined, representative sample, and very few include sufficient information to allow the calculation of a response rate. From a traditional research perspective, the absence of these indicators undermines confidence in the generalizability of the findings reported here. Pragmatic research defines itself as identifying useful answers to research questions ( Newton et al., accepted). From this perspective then, we considered it useful to still proceed with an analysis of these studies, and consider the findings holistically. It is useful for the research community to be aware of the limitations of these studies, and we report on these measures of study quality in Appendix 1. We also think it is useful to report on the evidence, within our findings, of a lack of clarity regarding what is actually meant by the term "Learning Styles." Taken all together these analyses could prompt further research, using a large representative sample with a high response rate, using a neutral invitation, with a clear explanation of the difference between Learning Styles and styles of Learning. Perhaps most importantly this research should focus on whether educators act on their belief, as described above.

Some of these limitations, in particular those regarding representative sampling, are tempered by the number of studies and a consistency in the findings between studies, and the overall very high rates of self-reported belief in Learning Styles. Thirty-four samples report on this question, and in all studies, the majority of participants agree with the key question. In 25 of the 34 samples, the rate of agreement is over 80%. Even if some samples were not representative, it would seem unlikely to affect the qualitative account of the main finding (although this may be undermined by the other limitations described above).

A summary conclusion from our findings then is that belief in matching instruction to Learning Styles is high and has not declined, even though there is currently no evidence to support such an approach. There are a number of methodological issues which might affect that conclusion, but when taken all together these are insufficient to completely alleviate the concerns which arise from the conclusion; a substantial majority of educators state belief in a technique for which the lack of evidence was established in 2004. In the final section of the discussion here we then consider, from a pragmatic perspective, what are the useful things that we might do with these findings, and consider what could be done to address the concerns which arise from them.

Our findings present some limited evidence that training has some effect on belief in matching instruction to Learning Styles. Only four studies looked at training, but in those studies the percentage who reported that belief in the efficacy of matching instruction to individual Learning Styles dropped from 78.4 to 37.1%. It seems reasonable to conclude that there is a risk of social desirability bias in these studies; if participants have been given training which explains the lack of evidence to support Learning Styles, then they might be reasonably expected to disagree with a statement which supports matching. Even then, for 37.1% of participants to still report that they believe this approach is effective is potentially concerning; it still represents a substantial number of educators. Perhaps more importantly these findings are, like many others discussed here, a self-report of a belief, rather than a measure of actual behavior.

There is already a substantial body of literature which identifies Learning Styles as a neuromyth, or an "urban legend." A 2018 study analyzed the discourse used in a sample of this literature and concluded that the language used reflected a power imbalance wherein "experts" told practitioners what was true or not. A conclusion was that this language may not be helpful if we truly want to address this widespread belief in a method that is ineffective ( Smets and Struyven, 2018). We have previously proposed that a "debunking" approach is unlikely to be effective ( Newton and Miah, 2017). It takes time and effort to identify student learning styles, and much more effort to then try and design instruction to match those styles. The sorts of instructors who go to that sort of effort are likely to be motivated by a desire to help their students, and so to be told that they have been propagating a "myth" seems unlikely to be news that it is well received.

Considering these limitations from a pragmatic perspective, it does not seem that training, or debunking, is a useful approach to addressing widespread belief in Learning Styles. It is also difficult to determine whether training has been effective when we have limited data regarding the actual use of Learning Styles theory. It may be better to focus on the promotion of techniques that are demonstrably effective, such as retrieval practice and other simple techniques as described in the introduction. There is evidence that these are currently lacking from teacher training ( National Council on Teacher Quality, 2016). Many evidence-based techniques are simple to implement, for example the use of practice tests, the spacing of instruction, and the use of worked examples ( Young et al., 2014; Weinstein et al., 2018). Concerns exist about the generalizability of education research findings to specific contexts, but these concerns might be addressed by the use of a pragmatic approach ( Newton et al., accepted).

In summary then, we find a substantial majority of educators, almost 90%, from samples all over the world in all types of education, report that they believe in the efficacy of a teaching technique that is demonstrably not effective and potentially harmful. There is no sign that this is declining, despite many years of work, in the academic literature and popular press, highlighting this lack of evidence. To understand this fully, future work should focus on the objective behavior of educators. How many of us actually match instruction to the individual Learning Styles of students, and what are the consequences when we do? Does it matter? Should we instead focus on promoting effective approaches rather than debunking myths?

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material, further inquiries can be directed to the corresponding author/s.

Author Contributions

PN conceived and designed the study, undertook searches, extracted data, undertook analysis, drafted manuscript, and finalized manuscript. AS re-extracted data and provided critical comments on the manuscript. AS and PN undertook PRIMSA quality analyses.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to acknowledge the assistance of Gabriella Santiago and Michael Chau who undertook partial preliminary data extraction on a subset of papers identified in an initial search. We would also like to thank Prof Greg Fegan and Dr. Owen Bodger for advice and reassurance with the analysis.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2020.602451/full#supplementary-material

References

Duram (2010). In L. A. Pragmatic Study, Encyclopedia of Research Design. Thousand Oaks, CA: SAGE Publications, Inc.

Newton, P., Da Silva, A., and Berry, S. (accepted). The case for pragmatic evidence-based higher education; a useful way forward? Front. Educ. doi: 10.3389/feduc.2020.583157
CrossRef Full Text

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How Common Is Belief in the Learning Styles Neuromyth, and Does It Matter? A Pragmatic Systematic Review

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A commonly cited use of Learning Styles theory is to use information from self-report questionnaires to assign learners into one or more of a handful of supposed styles (e.g., Visual, Auditory, Converger) and then design teaching materials that match the supposed styles of individual students.

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A Powerful Combo: Diversity, Equity, and Belonging (DEB) and Mind, Brain, and Education (MBE)

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A Powerful Combo: Diversity, Equity, and Belonging (DEB) and Mind, Brain, and Education (MBE)

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"Together, DEB and MBE are a powerful combination for great teaching and making learning more equitable-and better for every student, not just people of color, not just students with learning differences."

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Video showcase to see how creative professionals around the globe are infusing technology with learning

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Virtually Inspired: Showcasing Online Learning Innovation

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An ongoing research project to uncover the best of breed technology-enhanced online courses and programs indicative of the "Online Classroom of the Future."

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75 Digital Tools and Apps Teachers Can Use to Support Formative Assessment in the Classroom

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75 Digital Tools and Apps Teachers Can Use to Support Formative Assessment in the Classroom

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List of tools divided by categories:
- Record audio and video
- Create quizzes, polls, and surveys
- Brainstorm, mind map, and collaborate
- Present, engage, and inspire
- Generate word or tag clouds
- Get real-time feedback
- Foster family communication
- Strengthen teacher-to-student or student-to-student communication
- Keep the conversation going with live chats
- Create and store documents or assignments

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There is no shortage of formative assessment strategies, techniques, and tools available to teachers who use formative instructional practice in their classrooms. Here is an extensive list of 75 digital tools, apps, and platforms that can help you and your students use formative assessment to elicit evidence of learning. We didn't just add any old tool to this list. Here are the criteria we used for those that made the cut:

Supports formative instructional strategies and ways to activate learners to be resources for themselves and peers

Is free or awful close to it (under $10 per year, where possible)

Allows both students and teachers to take the activator role when possible (sometimes teachers need to get things started)

Before you dig into the tools, I encourage you to spend some time thinking about exactly what you want to accomplish with your students. "How to pick the right digital tool: Start with your learning goal" by Erin Beard can help you wrap your head around goals and guide you in choosing the best tool for the task. "27 easy formative assessment strategies for gathering evidence of student learning" can help you decide what strategies work best for you and your students.

Record audio and video

Create quizzes, polls, and surveys

1. Crowdsignal Lets you create online polls, quizzes, and questions. Students can use smartphones, tablets, and computers to provide their answers, and information can be culled for reports.

2. Edulastic Allows you to make standards-aligned assessments and get instant feedback.

3. FreeOnlineSurveys Helps you create surveys, quizzes, forms, and polls.

4. Gimkit Lets you write real-time quizzes. And it was designed by a high school student!

5. Kahoot! A game-based classroom response system that lets you create quizzes using internet content.

6. MicroPoll Helps you create polls, embed them into websites, and analyze responses.

7. Naiku Lets you write quizzes students can answer using their mobile devices.

8. Obsurvey Designed to make surveys, polls, and questionnaires.

9. Poll Everywhere Lets you create a feedback poll or ask questions and see results in real time. Allows students to respond in various ways. With open-ended questions, you can capture data and spin up tag clouds to aggregate responses.

10. Poll Maker Offers unique features, like allowing multiple answers to one question.

11. ProProfs Helps you make quizzes, polls, and surveys.

12. Quia Lets you create games, quizzes, surveys, and more. Access a database of existing quizzes from other educators.

13. Quizalize Helps you create quizzes and homework.

14. Quizizz Guides you through designing quizzes and lets you include students in the quiz-writing process.

15. Quizlet Lets you make flashcards, tests, quizzes, and study games that are mobile friendly.

16. Survey Hero Designed to build questionnaires and surveys.

17. SurveyMonkey Helpful for online polls and surveys.

18. SurveyPlanet Also helpful for online polls and surveys.

19. Triventy Lets you create quizzes students take in real time using individual devices.

20. Yacapaca Helps you write and assign quizzes.

21. Zoho Survey Allows you to make mobile-friendly surveys and see results in real time.

Brainstorm, mind map, and collaborate

Present, engage, and inspire

Generate word or tag clouds

1. EdWordle Generates word clouds from any entered text to help aggregate responses and facilitate discussion. Word clouds are pictures composed of a cloud of smaller words that form a clue to the topic.

2. Tagxedo Allows you to examine student consensus and facilitate dialogues.

3. Wordables Helps you elicit evidence of learning or determine background knowledge about a topic.

4. WordArt Includes a feature that allows the user to make each word an active link to connect to websites, including YouTube.

Get real-time feedback

Foster family communication

1. Remind Lets you text students and stay in touch with families.

2. Seesaw Helps you improve family communication and makes formative assessment easy, while students can use the platform to document their learning.

3. Voxer Lets you send recordings so families can hear how their students are doing, students can chat about their work, and you can provide feedback.

Strengthen teacher-to-student or student-to-student communication

Keep the conversation going with live chats

1. Yo Teach A backchannel site great for keeping the conversation going with students.

2. Chatzy Supports live, online chats in a private setting.

Create and store documents or assignments

1. Google Forms A Google Drive app that allows you to create documents students can collaborate on in real time using smartphones, tablets, and laptops.

2. Piazza Lets you upload lectures, assignments, and homework; pose and respond to student questions; and poll students about class content. This tool is better suited for older students as it mimics post-secondary class instructional formats.

Learn more

There are several resources for learning more about formative assessment and responsive instruction strategies. Consider our formative practices workshops, where school and district teams can gain a better understanding of the role formative practice plays in instruction and the four foundational practices to use in the classroom. Or for a quick start, download our eBook "Making it work: How formative assessment can supercharge your practice."

Jump in, try new tools and methods, and have fun!

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75 digital tools and apps teachers can use to support formative assessment in the classroom

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We've compiled an extensive list of 75 digital tools that can help teachers use formative assessment to elicit evidence of student learning.

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Reviews different online tools that can be used for teaching and learning

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University of Massachusetts Amherst

Torrey Trust, Ph.D. is a Professor of Learning Technology in the Department of Teacher Education and Curriculum Studies in the College of Education at the University of Massachusetts Amherst. Her work centers on the critical examination of the relationship between teaching, learning, and technology; and how technology can enhance teacher and student learning. Specifically, Dr. Trust studies how educators engage with digitally enhanced professional learning networks (PLNs), how emerging pedagogical tools (e.g., HyperDocs), practices (e.g., Making) and technologies (e.g., 3D printers, augmented reality, ChatGPT, generative AI tools) influence learning, how to design and use open educational resources (OERs), and how to find, critically evaluate, and teach with digital tools and apps. Dr. Trust served as a professional learning network leader for the International Society for Technology in Education (ISTE) for five years, including a two-year term as the President of the Teacher Education Network from 2016 to 2018.

Dr. Trust's research, teaching, and service in the field of educational technology has received noticeable recognition, including the 2016 ISTE Online Learning Network Award, 2017 Journal of Digital Learning in Teacher Education Outstanding Research Paper Award, 2017 American Educational Research Association (AERA) Instructional Technology SIG Best Paper Award, 2017 ISTE Emerging Leader Award, 2019 AERA Technology as an Agent of Change for Teaching & Learning SIG Early Career Scholar Award, 2020 University of Massachusetts Amherst College of Education Outstanding Teaching Award, 2020 AECT Annual Achievement Award, 2023 MERLOT Classics Award, and the 2023 University of Massachusetts Amherst Distinguished Teaching Award. In 2018, Dr. Trust was selected as one of the recipients for the ISTE Making IT Happen Award, which "honors outstanding educators and leaders who demonstrate extraordinary commitment, leadership, courage and persistence in improving digital learning opportunities for students." www.torreytrust.com

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This eBook features critical reviews of more than 100 digital tools and apps for teaching and learning. Each page was written and designed by students enrolled in EDUC 390D, 592A, or 692D at the University of Massachusetts Amherst

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"The purpose of low-stakes assignments is to provide students with an indication of their performance while taking a course and give students an opportunity to improve their performance prior to receiving a final grade, either on an assignment or in a course."
Examples: breaking down larger assignments, quizzes, journaling, writing

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Low-stakes assignments are forms of evaluation that do not heavily impact students' final grades or other educational outcomes. The purpose of low-stakes assignments is to provide students with an indication of their performance while taking a course and give students an opportunity to improve their performance prior to receiving a final grade, either on an assignment or in a course.

As Vincent Tinto writes in Completing College: Rethinking Institutional Action (2012), "To be effective, assessments must be frequent, early, and formative." At colleges and universities recognized by the National Survey on Student Engagement for their success in promoting student's active engagement in their learning: "Feedback from faculty to students is timely and frequent, as documented both by NSSE data and by interviews with students and faculty members" (Kuh, et al. 2010).

Low-stakes assignments tend to work best when they generate formative feedback regarding where students are in the course, what they are doing well, and where they may need development to ultimately succeed in the class. At DePaul, there is a general expectation that students will be given feedback early in the quarter or term, and low-stakes assignments are a powerful method of doing so.

For students in their first year, whether as freshmen or transfers, such feedback is especially important in order to help them understand the expectations at DePaul. Early feedback also enables faculty to use DePaul's BlueStar system to identify students who may need additional assistance and share that information with the students and their academic advisors.

Benefits of Low-Stakes Assignments

Low-stakes assignments

Give students a realistic idea of their performance early in the term, enabling them to seek appropriate resources as needed

Open up lines of communication between students and their instructors, and may increase students' willingness to ask for help

Provide feedback for instructors on how well students are absorbing information and progressing in their skill development

Allow instructors to direct students to resources if they need further assistance or support

Give students an opportunity to be active participants in the evaluation of their own learning

Increase the likelihood that students will attend class and be active and engaged

Low Stakes-Assignments in Online and Flex Modality Courses

While all classes can benefit from frequent low-stakes assignments, it's especially important in online and Flex modalities. In a face-to-face classroom, instructors may assess student learning in informal ways using nonverbal cues-a puzzled look or an affirming nod, for example. In a online and Flex courses, instructors may have more difficulty picking up these cues from students, especially if they don't always have their cameras on or you're not meeting synchronously. In Flex classes, it can also be more challenging to pick up on nonverbal cues among in-person students if also trying to monitor engagement and participation among remote students at the same time. ​​

D2L Tools for Low-Stakes Assignments

​D2L tools will help you to implement low-stakes assignments in all types of courses and course modalities.

Quizzes are often used for multiple-choice or other objective question types because they can be graded automatically, allowing students to get feedback immediately. For low-stakes assignments, you may want to allow students to review the questions and their answers after they take the quiz. You can set up Submission Views to allow for this. Instructors can also view quiz statistics to see if the class is struggling with a particular concept and review that topic with them.

Discussions

Discussions allow students to share short pieces of writing or other media with you and their classmates. They work well for sharing rough drafts or other early-stage components of larger projects for peer review or for certain journaling assignments.

Submissions

Submission folders allow students to submit work privately to you. You may be used to using Submissions for major assignments like research papers. For shorter, less formal pieces of writing, you can change the Submission Type to "Text" to allow students to type their response directly into D2L rather than uploading a file from their computer.

Examples of Low-Stakes Assignments

Though given less weight, low-stakes assignments may be similar in type and kind to high-stakes assignments: they tend to reflect the kind of work students are going to be expected to do for a final exam, paper, or other summative project.

Components of a Larger Project

When assigning students a writing or research project, break down the elements of the project and use one or more as a low-stakes assignment. Require students to submit their works-in-progress so that they can receive early written feedback and a small grade, which could consist simply of a check or check-minus. Any one (or more) of the following elements could be collected and used as a low-stakes assignment:

Prospectus or proposal

Abstract

Thesis statement

Outline

Annotated bibliography

Specific sections of the final project (e.g., introduction, methods, lit. review)

Early-stage drafts of a paper

Early Drafts and Peer Review

Midway through a writing project, have students bring a full or partial draft of their paper to class and then exchange feedback with a peer. Students will receive valuable feedback that they can use to revise their work.

Try providing students with a rubric to help them give their peers targeted, assignment-specific feedback. Also, consider inviting Writing Center tutors to your class to model how peer review can be conducted effectively.

Mid-Project/Quarter Conferences

Around midterm or during the planning stages of a major course project, ask students to meet with you for conferences. If the class is too large for you to meet with each student individually, assign students to meet with you in groups, or one-on-one with their TA. The conference time might be dedicated to discussing the students' progress towards course goals or providing feedback on a particular project.

To get the most out of your conference time, ask students to complete and bring with them a self-assessment form or project proposal. Alternatively, ask students to write down one question they have about the course, content covered in class, or an assignment they are currently working on. Having a document to reference will keep the conversation on track and help put both you and your students at ease.

Group Work Planning and Reflection

When students are working in groups, require them to submit a statement outlining each group members' responsibilities and/or a timeline that measures their progress. Periodically throughout the group project, ask students to submit short reflections detailing their progress and any areas where they need support.

Weekly Quizzes

For a course where exams are the primary means of summative assessment, give students a quiz at the end of each week with questions based on content covered up to that point. Although the quiz might not count for credit (or might only count for a very small portion of the final grade), it will give students an idea of what they already learned and which concepts they need to spend more time with. Quizzes can be paper-based, conducted during class using Polling or posted online in D2L Quizzes.

Class Problem Solving

In a course where students learn new computational or mathematical concepts, have a problem posted on the board or screen at the start of each class, or in a collaborative document. Students can work individually, in pairs, or in small groups to find the solution. Spend part of class going over the problem as a big group, or review the problem in a short video.

Course Concept Journals

Consider having students keep a regular journal where they can engage with and apply course concepts. For example, in an Introduction to Political Science course, ask students to read the politics section of the New York Times and keep a weekly concept-application journal. For each entry, students should select one article that they read, summarize it, and show how the article demonstrates a theoretical concept discussed that week in class. Review students' journals every other week or so and give each entry brief feedback.

Reading Journals

In a course where students are required to do weekly readings, assign a reading journal. Entries might require students to summarize and respond to the source, or to answer a set of questions provided by the instructor. Students can submit their journal entries through D2L, by handing in hard copies every other week, or by posting them on a course blog.

Short Writing Assignments

Give students a prompt that corresponds to a class reading assignment, concept, or activity and have them turn in a short written response. Return the students' responses with your brief feedback, indicating whether the students are on track or not (a simple "check plus" or "check minus" could be used). In large classes where you might not have time to give individualized written feedback to each student, share and discuss one or two anonymous students responses at the next class meeting or in a D2L News item or weekly introduction.

Discussion Questions

Post open-ended questions designed to promote engagement with the current course content or an upcoming project or assignment. Invite students to ask questions and identify areas of confusion, and answer the questions posted by their peers. See Facilitating Discussions for additional suggestions.

Support for Low-Stakes Assignments at DePaul

University Center for Writing-based Learning (UCWbL)
Discuss how you can incorporate writing in a low-stakes assignment by contacting Matthew Pearson, Director of the UCWbL. You can also invite peer tutors to your classroom to demonstrate effective peer review before having your own students give feedback to one another.

University Libraries
If you're interested in using an element of a research project as a means of giving early feedback to students, connect with a university librarian. University librarians can offer feedback and resources for designing research projects for students.

Center for Teaching and Learning (CTL)
Have a question about integrating discussion forums in your classes? Not sure which platform might be best for setting up a course blog? Contact the CTL for help identifying and implementing a technology tool to help meet your goals.

Further Reading

Angelo, T., & Cross, K.P. (1993). Classroom assessment techniques. San Francisco: Jossey-Bass, Inc.

Bean, J. C. (1996). Engaging ideas: the professors guide to integrating writing,critical thinking and active learning in the classroom. San Francisco: Jossey-Bass Publishers.

Kuh, G. D., Kinzie, J., Schuh, J.S., and Whitt, E.J. (2010). Student success in college: Creating conditions that matter. San Francisco, CA: Jossey-Bass.

Lang, J. M. (2013). Cheating Lessons: Learning from Academic Dishonesty. Cambridge, MA: Harvard University Press.

Nilson, L. B. (2010). Teaching at its best: A research-based resource for college instructors. San Francisco: Jossey-Bass.

Tinto, Vincent. (2012). Completing college: Rethinking institutional action. Chicago, IL: University of Chicago Press.

Warnock, Scott. (2013). Frequent, low-stakes grading: Assessment for communication, confidence. Faculty Focus. Madison, WI: Magna Publications.

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Low-stakes assignments are forms of evaluation that do not heavily impact students' final grades or other educational outcomes. The purpose of low-stakes assignments is to provide students with an indication of their performance while taking a course and give students an opportunity to improve their performance prior to receiving a final grade, either on an assignment or in a course.

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Formative: in-class discussions, clicker questions, low-stakes group work, weekly quizzes, 1-minute reflection writing assignments, homework assignments, surveys
Summative: instructor-created exams, standardized tests, final projects, final essays, final presentations, final reports, final grades

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Examples of Formative and Summative Assessments

Both forms of assessment can vary across several dimensions (Trumbull and Lash, 2013):

Informal / formal

Immediate / delayed feedback

Embedded in lesson plan / stand-alone

Spontaneous / planned

Individual / group

Verbal / nonverbal

Oral / written

Graded / ungraded

Open-ended response / closed/constrained response

Teacher initiated/controlled / student initiated/controlled

Teacher and student(s) / peers

Process-oriented / product-oriented

Brief / extended

Scaffolded (teacher supported) / independently performed

Recommendations

Formative Assessment Ideally, formative assessment strategies improve teaching and learning simultaneously. Instructors can help students grow as learners by actively encouraging them to self-assess their own skills and knowledge retention, and by giving clear instructions and feedback. Seven principles (adapted from Nicol and Macfarlane-Dick, 2007 with additions) can guide instructor strategies:

Keep clear criteria for what defines good performance - Instructors can explain criteria for A-F graded papers, and encourage student discussion and reflection about these criteria (this can be accomplished though office hours, rubrics, post-grade peer review, or exam / assignment wrappers). Instructors may also hold class-wide conversations on performance criteria at strategic moments throughout a term.

Encourage students' self-reflection - Instructors can ask students to utilize course criteria to evaluate their own or a peer's work, and to share what kinds of feedback they find most valuable. In addition, instructors can ask students to describe the qualities of their best work, either through writing or group discussion.

Give students detailed, actionable feedback - Instructors can consistently provide specific feedback tied to predefined criteria, with opportunities to revise or apply feedback before final submission. Feedback may be corrective and forward-looking, rather than just evaluative. Examples include comments on multiple paper drafts, criterion discussions during 1-on-1 conferences, and regular online quizzes.

Encourage teacher and peer dialogue around learning - Instructors can invite students to discuss the formative learning process together. This practice primarily revolves around mid-semester feedback and small group feedback sessions, where students reflect on the course and instructors respond to student concerns. Students can also identify examples of feedback comments they found useful and explain how they helped. A particularly useful strategy, instructors can invite students to discuss learning goals and assignment criteria, and weave student hopes into the syllabus.

Promote positive motivational beliefs and self-esteem - Students will be more motivated and engaged when they are assured that an instructor cares for their development. Instructors can allow for rewrites/resubmissions to signal that an assignment is designed to promote development of learning. These rewrites might utilize low-stakes assessments, or even automated online testing that is anonymous, and (if appropriate) allows for unlimited resubmissions.

Provide opportunities to close the gap between current and desired performance - Related to the above, instructors can improve student motivation and engagement by making visible any opportunities to close gaps between current and desired performance. Examples include opportunities for resubmission, specific action points for writing or task-based assignments, and sharing study or process strategies that an instructor would use in order to succeed.

Collect information which can be used to help shape teaching - Instructors can feel free to collect useful information from students in order to provide targeted feedback and instruction. Students can identify where they are having difficulties, either on an assignment or test, or in written submissions. This approach also promotes metacognition, as students are asked to think about their own learning. Poorvu Center staff can also perform a classroom observation or conduct a small group feedback session that can provide instructors with potential student struggles.

Instructors can find a variety of other formative assessment techniques through Angelo and Cross (1993), Classroom Assessment Techniques (list of techniques available here).

Summative Assessment Because summative assessments are usually higher-stakes than formative assessments, it is especially important to ensure that the assessment aligns with the goals and expected outcomes of the instruction.

Use a Rubric or Table of Specifications - Instructors can use a rubric to lay out expected performance criteria for a range of grades. Rubrics will describe what an ideal assignment looks like, and "summarize" expected performance at the beginning of term, providing students with a trajectory and sense of completion.

Design Clear, Effective Questions - If designing essay questions, instructors can ensure that questions meet criteria while allowing students freedom to express their knowledge creatively and in ways that honor how they digested, constructed, or mastered meaning. Instructors can read about ways to design effective multiple choice questions.

Assess Comprehensiveness - Effective summative assessments provide an opportunity for students to consider the totality of a course's content, making broad connections, demonstrating synthesized skills, and exploring deeper concepts that drive or found a course's ideas and content.

Make Parameters Clear - When approaching a final assessment, instructors can ensure that parameters are well defined (length of assessment, depth of response, time and date, grading standards); knowledge assessed relates clearly to content covered in course; and students with disabilities are provided required space and support.

Consider Blind Grading - Instructors may wish to know whose work they grade, in order to provide feedback that speaks to a student's term-long trajectory. If instructors wish to provide truly unbiased summative assessment, they can also consider a variety of blind grading techniques.

Considerations for Online Assessments

Effectively implementing assessments in an online teaching environment can be particularly challenging. The Poorvu Center shares these recommendations.

References

Nicol, D.J. and Macfarlane-Dick, D. (2006) Formative assessment and self‐regulated learning: a model and seven principles of good feedback practice. Studies in Higher Education 31(2): 2-19.

Theall, M. and Franklin J.L. (2010). Assessing Teaching Practices and Effectiveness for Formative Purposes. In: A Guide to Faculty Development. KJ Gillespie and DL Robertson (Eds). Jossey Bass: San Francisco, CA.

Trumbull, E., & Lash, A. (2013). Understanding formative assessment: Insights from learning theory and measurement theory. San Francisco: WestEd.

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Examples of Formative and Summative Assessments Examples of Formative and Summative Assessments Formative Summative In-class discussions Instructor-created exams Clicker questions Standardized tests Low-stakes group work Final projects

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Practice for Knowledge Acquisition (Not Drill and Kill)

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Practice for Knowledge Acquisition (Not Drill and Kill)

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"Teachers should always design practice activities with the goal of transferring knowledge to new and more complex problems in mind."

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Cite this

Brabeck, M., & Jeffrey, J. (2015, March 9). Practice for knowledge acquisition (not drill and kill). American Psychological Association. https://www.apa.org/education-career/k12/practice-acquisition

Designing activities with the goal of transferring knowledge

It doesn't matter what subject you teach, differences in students performance are affected by how much they engage in deliberate practice. Researchers who have investigated expert and novice performance have uncovered important distinctions between deliberate practice and other activities, such as play or repetition.

Deliberate practice is not the same as rote repetition. Rote repetition - simply repeating a task - will not by itself improve performance. Deliberate practice involves attention, rehearsal and repetition and leads to new knowledge or skills that can later be developed into more complex knowledge and skills. Although other factors such as intelligence and motivation affect performance, practice is necessary if not sufficient for acquiring expertise (Campitelli & Gobet, 2011).

Define practice:

"Deliberate practice (DP) occurs when an individual intentionally repeats an activity in order to improve performance. The claim of the DP framework is that such behavior is necessary to achieve high levels of expert performance." (Campitelli & Gobet, 2011, p. 280).

"Deliberate practice consists of activities purposely designed to improve performance." (Gobet & Campitelli, 2007, p. 160).

Benefits of deliberate practice

Practice is important for teaching and learning in at least five ways:

1. Practice greatly increases the likelihood that students will permanently remember new information (Anderson, 2008).

2. Practice increases student facility or automaticity (learning to apply elements of knowledge automatically, without reflection). Automaticity is usually only achieved through extensive rehearsal and repetition. Automaticity frees up students cognitive resources to handle more challenging tasks (Brown & Bennett, 2002; Moors & De Houwer, 2006).

3. When students practice solving problems, they increase their ability to transfer practiced skills to new and more complex problems. This is true in childhood (Glover, Ronning, & Bruning, 1990) and adult years (Li, Schmiedek, Huxhold, Röcke, Smith, & Lindenberger, 2008).

4. Practice helps students acquire expertise in subject matter and, therefore, it helps to distinguish novices from experts in given subjects (Ericsson, Krampe, & Clemens, 1993)

5. Cognitive gains from practice often bring about motivation for more learning (Kalchman, Moss, & Case, 2001).

Teachers should think of practice not as rote repetition, but as deliberate, goal-directed rehearsal paired with reflection on problem-solving processes. For example, when teachers have students practice identifying phonemes, the ultimate goal is for students to read with fluency and comprehension. Although fluent reading may be too complex a task for beginning readers to tackle, the more manageable task of identifying phonemes may scaffold students learning to achieve the ultimate goal of reading. That is, teachers should always design practice activities with the goal of transferring knowledge to new and more complex problems in mind.

General recommendations for practice and instruction

Research (Anderson, 2008; Campitelli & Gobet, 2011; Ericsson, Krampe, & Clemens, 1993) suggests several conditions that must be in place in order for practice activities to be most effective in moving students closer to skillful performance. Each of these conditions can be met with carefully designed instruction:

Because practice requires intense, focused effort, students may not find it inherently enjoyable. Therefore, teachers can encourage students to practice more by pointing out every time that practice has actually improved their performance. Motivate your students to practice by:

Expressing confidence in students' ability to succeed in solving practice problems.

Designing activities that maximize opportunities for students to succeed.

Teachers should design practice tasks with students existing knowledge in mind. When students succeed at practice problems, the benefits of practice are maximized. But when students become frustrated with unrealistic or poorly designed practice problems, they often lose motivation, will not receive the full benefits of the practice they have done, and will be less motivated to attempt future practice problems.

Students receive the greatest benefits from practice when teachers provide them with timely and descriptive feedback.

Students should have repeated opportunities to practice a task through practicing other tasks like it.

What type of feedback is most effective?

Feedback is most effective when it contains descriptions of how students work meets performance criteria and what students can do to improve. This kind of descriptive feedback is more effective than feedback consisting of vague, general comments (e.g., "nice work or "needs improvement).

Feedback should also be focused on the learning process. That is, teachers should focus their feedback on helping students reflect on their problem solving skills, as well as on progress they have made.

Do's:

1. Vary practice activities.

2. Distribute practice over extended periods of time.

3. Provide clear instructions on performance expectations and criteria.

4. Before asking students to practice independently, model the problem-solving process that you expect students to use.

5. Break complex problems into their constituent elements, and have students practice on these smaller elements before asking them to solve complex problems independently.

6. Guide students through sample practice problems by using prompts that help them reflect on problem-solving strategies.

7. Provide students with fully completed sample problems as well as partially completed sample problems before asking them to apply new problem-solving strategies on their own.

8. Wait until students actually need more information to solve a complex problem before you give them more information. This strategy - known as "just-in-time instruction" - helps keep the amount of information that students must hold in their short-term memories to a manageable level as they practice (Linneman & Plake, 2006; Simkins & Maier, 2008).

9. Provide plenty of opportunities for students to practice applying problem-solving skills before you test them on their ability to use those skills.

Don'ts:

1. Don't ask students to practice complex problem solving without providing them with plenty of guidance and feedback.

2. Don't overload students by presenting information in redundant formats.

3. Don't give your students complex practice problems before they have the skills they need to succeed.

Research suggests that when teachers construct problems for students to practice, they should keep in mind the limitations of human memory.

Moving information to permanent storage is often explained as a multistore model of memory (Atkinson & Shiffrin, 1968; 1971; Baddeley, 1996; 2002). According to this model, our brains have three memory storage systems: sensory memory, short-term memory and long-term memory (Baddeley, 1996; 2002; Carlson, Sullivan, & Schneider, 1989). Learning occurs when we move information from working memory to long-term memory, and practice helps with this process.

Deliberate practice (sometimes called 'rehearsal') keeps the information in our short-term memory long enough for it to move to long-term memory. Once it is in long-term memory, it can be built upon to create more and more complex associations (Baddeley, 2002).

Most researchers agree that there are forms of Short-Term Memory (STM) for different modalities, such as visual STM, auditory STM and motor STM. For example, Baddeley (1992; 1996; 2002) suggests that short-term memory is a dynamic place where sounds and images are turned into verbal and pictorial models. STM is also thought to have a "central executive" that manages the information it is holding. This central executive also controls our awareness of that information (Baddeley, 2002).

The implication for teachers is that they should present material in multiple modalities. If a child who is learning to read hears a word, sees the word and a picture of the word, there will be more learning. The child can get the visual information from the visual STM and additional auditory information in the auditory STM. These two sensory sources of information supplement and complement one another (Liao, 2014). However, teachers must be careful not to have too much extraneous information in the classroom. Over stimulation can put too much demand on working memory (Pass & Kester, 2006).

Short-term or working memory

Working memory or short-term memory, is stored for very short periods of time - minutes at most. Information in our short-term memory is generally housed in the temporal lobe of the brain (the hippocampal region). It allows you to remember phone numbers and also information that comes from long-term memory right before you need to use it. Short-term memory is also the memory system that is used when we cram for a test at the last minute. Attention difficulties, distractions and overload of too much information all negatively impact our short-term memory and cause it to fail. We all have a limited ability to focus on too much stimuli at the same time, and if we don't process the information, we lose it. Practice helps us increase our ability to access information rapidly and automatically.

Practice also frees our brains to process more challenging information and problems. For example, if a child has memorized the multiplication table and can retrieve it automatically, working memory is freed to do more complicated computations.

Practice can improve children's working memory and enhance learning of academic skills, such as reading comprehension and mathematical reasoning (e.g., Dahlin, 2011; Gersten, Fuchs, Williams, & Baker, 2001; Holmes, Gathercole, & Dunning, 2009), verbal learning (Liao, Kronemer, Yau, Desmond, & Marvel, 2014) and motor skills (Gomes, Ugrinowitsch, Marinho, Shea, Raisbeck, & Benda, R., 2014).

And practice using visual spatial tasks can improve visual-spatial working memory (Melby-Lervag & Hulme, 2012; Shipstead, Redick, & Engle, 2012). Likewise, children can be taught a rehearsal or practice strategy to help them improve their reading comprehension (e.g., Gersten, Fuchs, Williams, & Baker, 2001; Rose, Cundick, & Higbee, 1983). Moreover, rehearsal is easier to learn and less demanding than other strategies (Turley-Ames & Whitfield, 2003).

Long-term memory

When working-memory is stored, it becomes long-term memory. Long-term memory is the depository of memories throughout the brain that results from the ongoing chemical processes that changes neuronal connections. The formation of long-term memories takes place over the span of days, weeks and in some cases years. As the memories are consolidated, neuron connections become more and more efficient, and in the process, connections that are no longer useful are discarded from lack of use.

Although the capacity of our long-term memory is apparently limitless (as far as we know, no one has ever stored all that the brain is capable of storing), working memory can typically hold only seven items (plus or minus 2) at any given time, and these items "decay" rapidly (Miller, 1956; Baddeley, 2002).

Research on the brains of rats has shown that relatively short periods of practice can enhance the plasticity of the brain and actually change its structure (Rioult-Pedotti, Friedman, Hess, & Donoghue, 1998). Brain imaging has shown that even a brief lesson on new words changes brain circuits (Allami, Brovelli, Hamzaoui, Regragui, Paulignan, & Boussaoud, 2014; Abdullaev & Posner, 1998; Raichle et al., 1994) and retrieval (Habib, Nyberg, & Tulving, 2003).

Experts and novices differ in the amount and structure of information stored in their long-term memories. These cognitive differences occur in large part because of differences in the amount of deliberate practice in which each has engaged (Cantor & Engle, 1993).

Extensive and deliberate practice makes it possible for students to access and apply increasingly complex information without explicitly thinking about it. In other words, it increases automaticity (see earlier discussion of automaticity) (Anderson, 2008; Case, 1985, 1991). Automaticity leaves students' working memories free to process new information (Kotovsky, Hayes, & Simon, 1985). Since working memory can be overloaded at any time, 'savings' from automaticity become very important. When there is too much information in our working memory it will fail, and the chemical processes that transfer information to our long-term memories also fail. Long-term learning is enhanced by a distribution process in which information is repeated, allowing time with time in between practice sessions, rather than crammed practice.

Reviews and tests are forms of practice that can improve learning. Tests (or quizzes) that are given immediately after a learning exercise give children opportunities to practice. Because learning is recent, students tend to do well on these tests. However, their success does not ensure long-term retention. Teachers can provide time during class to give students additional practice in taking tests. Some tests, such as tests with open-ended answers have been shown to enhance learning because they involve students in the "retrieval" of information from long-term memory. Tests are more effective when they are given at spaced intervals and when they are given frequently (Cull, 2000; Dempster; 1991; Rodriguez, Michael, 2004;Roediger & Karpicke, 2006).

Chunking information, or combining small bits together in short-term memory, is another strategy that students can use to increase their STM holding capacity. Although chunking gives a student more capacity in their STM, its usefulness depends on how much knowledge a person already has. The more knowledge one has on a topic, the more likely that one can "chunk" incoming information into larger and more meaningful chunks. This is why knowledge itself adds to one's ability to solve complex problems. Children who use chunking are more likely to be strategic about their learning. Teachers can help children become more strategic by explicitly teaching them strategies like chunking.

Example of chunking information

The following digits can be seen as a random list of numbers: 14921776. Random or meaningless information taxes the limited capacity of short-term memory (STM). However, if a student "chunks" the information into two dates (1492: Columbus discovered America; and 1776: the year the Declaration of Independence was signed) it is learned as 1492 1776. This leaves room in the short-term memory for more information.

Chunking allows several units of information to be compressed into a single meaningful unit or chunk (e.g., whereas a random number string above represents 8 discrete units to be stored in short-term memory (near its maximum capacity), the two meaningful dates represent only two chunks of information to be stored. This leaves space in the STM for more information to be held. And when more information is held in STM, there is more information available for transfer to long-term memory where it can be permanently stored (Gobet et al., 2001).

Chunking can develop experts' schema. Information stored in the brain in larger chunks of integrated concepts are known as "schemas." For example, a novice chess player who does not yet have complex schemas for chess playing has to devote his or her limited working memory to processing large amounts of information while playing (Anderson, 1996).

An expert, on the other hand, can easily access schemas from long-term memory (deGroot, 1966; Cantor & Engle, 1993). With practice, a novice player can construct chess-playing schemas and therefore increase the amount of information stored in long-term memory. In this way, practice accessing and applying knowledge helps one to transfer that knowledge into a "permastore" of long-term memory so that he or she can more efficiently utilize space available in the working memory (Elo, 1978; Kotovsky & Fallside, 1989).

Theories explaining expert performance

1. Many researchers (e.g., Ericsson et al., 1993; Baddeley, 2002) argue that differences in the amount of practice account for the majority of performance differences between individuals, resulting from differences in the amount and structure of knowledge stored in long-term memory.

2. Other researchers emphasize innate, genetic differences or talent as the main factor accounting for differences between expert and novice performance (Mosing, et.al., 2014; Plomin, DeFries, McClearn, & Rutter, 1997).

3. More recently, researchers have emphasized an interaction between innate and behavioral factors in accounting for differences in performance levels (Gobet & Campitelli, 2007).

Overall, the research supports the view that deliberate practice plays a role in performance differences and can reduce performance differences between individuals who are thought to possess talent and those who do not (Chase & Ericsson, 1981). See Gobet and Campitelli (2007), Mosing, et.al., (2014) and Macnamara, et.al., (2014) for a comprehensive review of this debate.

What can a teacher do to maximize the positive effect of practice?

When teachers assign students to complete practice problems that are structurally similar but different in surface features, it helps them discriminate between relevant and irrelevant information in a given problem. This outcome makes it more likely that students will be able to transfer knowledge gained from practice to new and more complex problems (Paas, 1992). As with all scaffolds, they should be diminished gradually as learners become increasingly more independent.

Students' problem-solving skills also increase when teachers distribute practice over time rather than "cramming" practice into short periods (Bahrick & Hall, 2005). Assignments involving practice are more effective when they are shorter, more frequent and distributed over longer periods of time (Cooper, 2007; Cooper, Robinson, & Patall, 2006; Epstein & Van Voorhis, 2010). This distributive effect is particularly important because it also makes it more likely that students will recall information over longer periods of time. Therefore, distributed practice helps students transfer information into their long-term memory.

Teachers should also have students begin homework during class. By doing so, teachers can monitor students to ensure they understand and can solve practice problems correctly before they practice independently. If teachers are unable to monitor this initial practice, they should explain to their students where they can obtain the information (textbooks, web sites, handouts, etc.) they need to complete the assigned practice problems correctly.

Reviews and tests are a form of practice that can improve learning. Tests provide opportunities for practice that enhance learning. Open-ended test questions also provide practice in retrieving information. Both reviews and tests are most effective when they are well spaced and frequent (Dempster, 1991).

Teachers can train students to reflect on their thinking when solving problems by providing them with lists of questions. This kind of "strategy instruction" helps students to construct schemas more efficiently by facilitating a metacognitive awareness of the problem-solving process. That is, students learn more about how they learn and this helps them with new learning.

Teachers can increase students' abilities to transfer existing problem-solving knowledge to new problems when they prompt students to deliberately reflect on problem-solving strategies they have used before. (Renkl, Stark, Gruber, & Mandl, 1998; Rosenshine & Meister, 1992; Stark, Mandl, Gruber, & Renkl., 2002).

Is homework always effective practice?

There is no question that homework can provide students with opportunities for practice but there are more and less effective homework assignments. Research on homework (Cooper, 2001, 2006; Epstein & Van Voorhis, 2010) suggests that the relationship between time spent on homework and students' academic achievement varies with grade level. When parental involvement is well structured it can also enhance the effectiveness of homework on learning (Epstein & Van Voorhis, 2010; Marzano & Pickering, 2007).

How much time should be spent on homework?

Although high school students appear to benefit from at least two hours of homework per night, there is little or no relationship between time spent on homework and academic achievement for young (grades 1-3) students (Marzano, & Pickering, 2007). Younger students may benefit more from supervised in-school practice than from homework. Middle school students may benefit from one to two hours of homework per night (Cooper, 2007).

Will practice activities negatively affect students' motivation? Aren't practice activities the same as "drill and kill" instruction?

Although deliberate practice is often confused with rote learning, researchers emphasize that there is a difference between mere repetition and deliberate practice. For practice to be effective, appropriate instruction, guided practice, as well as descriptive and timely feedback must all be present.

Does practice with "authentic" problems increase learning?

Due to their relevance, "authentic" problems or "real-life" problems may motivate students. However, the positive effect of practice is only realized when teachers provide instructional supports that address the limitations of students' working memories (Anderson, Reder, & Simon, 1996, 2000; Sweller, van Merrienboer, & Paas, 1998; van Merrienboer, Kirschner, & Kester, 2003). If there is cognitive overload and the working memory is taxed beyond its capacity, students will lose motivation even if a problem is highly relevant to them.

Although "authentic" practice problems sound like they would motivate students with their real-world context; they may also include too many variables at the same time. Students' working memories are thus required to manage too many bits of information, and many of these bits may not be directly involved with the construction of the desired schema (Sweller & Chandler, 1994).

Is there such a thing as too much practice?

Merely repeating a task will not automatically improve performance. Deliberate practice is most effective when it involves intense concentration, rehearsal and reflection. For that reason, students are more likely to benefit from frequent, short practice sessions than from long "cramming" sessions. Teachers can foster this meta-cognitive learning process by providing students with frequent opportunities to discuss their problem-solving strategies (National Research Council, 2005).

Even in early grades when students are acquiring foundational knowledge, practice should not be confused with rote learning. For example, phonics instruction is most effective when it is practiced alongside more complex skills like identifying main ideas in stories (National Institute of Child Health and Human Development, 2000).

Does practice work for all academic subjects?

Practice is correlated with student achievement across all developmental levels and across all subjects. NICHD's (2000) analysis of strategies for reading instruction suggests that several kinds of practice activities are beneficial to students. Phonemic awareness practice, phonics practice and read-aloud practice all showed significant positive effects on student achievement in the early grades. Teachers should always keep long-term goals in mind when they are designing practice activities. The ultimate purpose of phonics instruction is not for students to identify phonemes, but rather, to improve their reading comprehension. The goal of practice is to transfer acquired skills to new and more complex problems. Therefore, practice activities should always be designed with that transfer process in mind.

Studies in mathematics education show that practice is effective when teachers design appropriate practice problems, distribute them over time, and provide students with sufficient feedback. Practice is ineffective when it is not appropriately designed, not well distributed, and when adequate feedback is not provided.

Deliberate practice is also effective in learning complex skills from musical fluency to athletic expertise.

When does the strategy work?

All students benefit from well-designed, developmentally-appropriate deliberate practice activities. A meta-analysis (Macnamara, Hambrick, & Oswald, 2014) showed that the effects of deliberate practice are robust for games, music and sports, and modestly effective in education.

Deliberate practice can also provide a bridge over the gaps that exist between different achievement levels. Achievement gaps often exist because of unequal opportunities for students to engage in appropriate deliberate practice rather than unequal learning abilities. As children grow older, they become better able to benefit from instruction on appropriate strategies like rehearsal, reflection and "chunking" information.

Special populations benefit from practice activities that are designed to meet their needs (Chapman, & Mudar, 2014; Nunes, Barros, Evans, & Burman, 2014; Rose, Cundick, & Higbee, 1983). For example, research on children with language processing problems suggests that their difficulties are due to sensory perception rather than an inability to learn. One reason that language-learning impaired students have difficulty learning to read is that their phonemic recognition and spatial discrimination perceptions are impaired. Appropriate practice activities have been shown to help these students compensate for their difficulties (Merzenich et al., 1996). Cooper's (2001) examination of homework practice suggests that students with learning disabilities benefit most from short, skill-reinforcing activities that are carefully monitored (see also Marzano & Pickering, 2007).

Description of research on the use of practice activities with children with learning disabilities

An extensive meta-analysis of interventions for students with learning disabilities found that practice-drill-review instructional strategies were generally effective across academic subjects (Swanson, Hoskyn, & Lee, 1999). Another meta-analysis of inclusion settings found that time-on-task correlated with student achievement across subject areas (Brophy & Good, 1986). Therefore, practice activities appear to be a key factor in student achievement across grade levels, skill levels and academic domains.

In summary, well-designed practice activities result from careful planning where teachers:

Model problem-solving processes.

Design partially-completed examples on which students can practice.

Sequence activities logically.

Space practice activities appropriately.

Monitor student practice providing guidance and feedback.

Is practice effective across the age span?

Children grow more strategic as they get older. Younger children are less strategic - are less likely to rehearse, practice, or chunk spontaneously or to know how to do these things well. They are also less likely to monitor their own knowledge or to use metacognitive strategies. Younger children are less likely than older children to know what they need to rehearse or practice more. Therefore, teachers of younger children should introduce them to strategies such as rehearsal and practice, and in this way can help students improve their learning.

Deliberate practice is effective across all school age levels. Fundamental practice skills are more appropriate for earlier grades than it is for later grades. Teachers can focus younger students on the skills they will need later when they tackle more complex tasks. For example, phonemic awareness instruction is more effective for preschool students than for older elementary school students (NICHD, 2000), but it remains a skill that is very useful in later grades.

Teachers should design developmentally appropriate practice problems for students. Students' memories and attention spans develop with age (Gomez-Perez & Ostrosky-Solis, 2006; Lechuga, Moreno, Pelegrina, Gomez-Ariza, & Bajo, 2006). In general, older students have greater attention spans and greater memory capacity than younger students. This may account for developmental differences seen in the benefits of homework (Cooper, 2001; Muhlenbrook, Cooper, Nye, & Lindsay, 1999) and age differences in the amount of practice that learners engage in outside the classroom (Ericsson et al., 1993). Teachers should plan the type and amount of practice students are expected to do with these developmental differences in mind.

Research on expert performance shows that experts increase the amount of deliberate practice they engage in over time. For example, research by Ericsson and colleagues (1993) on expert violinists revealed that in early childhood they practiced no more than five hours per week. However, by their early twenties, they practiced around thirty hours per week. Because deliberate practice is so taxing, it should be balanced with sufficient periods of rest. Younger learners tire more quickly and lose their motivation to persist if they are not allowed sufficient rest (Ericsson et al., 1993; NICHD, 2000).

Elementary

Early elementary students benefit little or not at all from homework (Campitelli & Gobet, 2011; Cooper, 2001; Halford, Maybery, & Bain, 1986).

Expert violinists practiced fewer than 5 hours per week at the early childhood stage (Ericsson et al., 1993; Macnamara et al., 2014).

Research supports the idea that practice can help elementary students overcome processing deficits (Merzenich et al., 1996).

Practice with phonemic awareness has been shown to have a positive effect on reading outcomes in elementary readers through 6th grade. Effects were not as strong for disabled readers as they were for at-risk and normally progressing readers. The effects of phonemic-awareness instruction were strongest when it was provided in earlier grades (especially pre-K and kindergarten), in small group settings, in shorter training programs and to English-speaking students (NICHD, 2000).

Research suggests that systematic phonics instruction helps students learning to read. The effects of systematic phonics instruction were greater when it was provided in earlier grades (especially kindergarten and 1st grade) (Dahlin, 2011; Gersten, Fuchs, Williams, & Baker, 2001; NICHD, 2000).

A meta-analysis of intervention strategies for students with learning disabilities found that phonics instruction positively affects development of vocabulary, although the results on comprehension were unclear (Swanson et al., 1999). (The mean age for the study participants was 11; 78% of the studies targeted 9-14 year age range.).

Research suggests that repeated and guided-repeated oral reading strategies have a positive effect on reading performance through grade four, especially for non-impaired readers (Dahlin, 2011; Gersten, et al., 2001; Rose et al., 1983).

Swanson et al.'s (1999) meta-analysis suggests that "the teaching of whole language without due attention to decoding places LD children at greater risk than the use of more traditional programs" (p. 254).

Vocabulary instruction (both explicit and implicit) has a positive effect on vocabulary acquisition. Research indicates that students should be exposed to vocabulary repeatedly and in multiple contexts (NICHD, 2000).

Research suggests that reading comprehension strategy instruction (e.g., cognitive and meta-cognitive strategies) has a positive effect on reading comprehension and memory in grades 3-6. The effects were greater when strategy instruction was more intensive and when students were instructed in multiple strategies (NICHD, 2000).

Research suggests that "drill-repetition-practice" instructional strategies are beneficial to students with learning disabilities across all age groups and across all subjects (Swanson et al., 1999).

Middle School

Middle school students may benefit from one to two hours of homework per night (Cooper, 2001; Campitelli & Gobet, 2011).

Research suggests that partial-task practice problems are more effective than whole-task practice problems for some 13 year-old math students. Partial-task practice lowered the processing demands on working memory for all students, but it only increased learning for students with low prior knowledge. Students with high prior knowledge benefited more from whole-task practice (Ayres, 2006).

Research suggests that when modeling problem-solving strategies, the use of worked examples is beneficial for middle school students' writing performance. As with Ayres' (2006) study, however, the positive effects were limited to low-performing students. The results suggested that "low-level" writers benefited from worked-example practice problems, while "high-level" writers benefited from "goal-free" practice problems.

Research suggests that the use of repeated and guided-repeated oral reading strategies positively affect students' reading performance. This is especially true for students with reading problems (NICHD, 2000).

Programs aimed at encouraging students to read more (e.g., self-selected, silent reading) yielded no significant positive effects on reading achievement (NICHD, 2000).

Swanson et al.'s (1999) meta-analysis suggests that "the teaching of whole language without due attention to decoding places LD children at greater risk than the use of more traditional programs" (p. 254).

Results that support the positive effects of vocabulary instruction (both explicit and implicit) on vocabulary acquisition indicate that students should be exposed to vocabulary repeatedly and in multiple contexts (NICHD, 2000).

Research suggests that reading-comprehension-strategy instruction (e.g., cognitive and meta-cognitive strategies) has a positive effect on reading comprehension and memory in grades 3-6. The effects were greater when strategy instruction was more intensive and when students were instructed on multiple strategies (NICHD, 2000).

Research suggests that "drill-repetition-practice" instructional strategies are beneficial to students with learning disabilities across all age groups and across all subjects (Swanson et al., 1999).

High School

High school students appear to benefit from at least two hours of homework per night, (Campitelli & Gobet, 2011; Cooper, 2001).

Students may benefit from more practice with the transfer of component skills than from practicing whole-task transfer (Pollock, Chandler, & Sweller, 2002). Examples worked out in advance for students were more effective for algebra problem solving transfer than were conventional problems for 9th and 11th grade students (Sweller & Cooper, 1985).

Repeated and guided-repeated oral reading strategies positively affect reading performance. This is especially true for high school students with reading problems (NICHD, 2000).

Programs aimed at encouraging students to read more (e.g., self-selected, silent reading) yielded no significant positive effects on reading achievement (NICHD, 2000).

Although research supports a positive effect of vocabulary instruction (both explicit and implicit) on vocabulary acquisition in grades 9-11, few studies have been conducted at that level. Results that do exist suggest that students should be exposed to vocabulary repeatedly and in multiple contexts (NICHD, 2000).

Research suggests that "drill-repetition-practice" instructional strategies are beneficial to students with learning disabilities across all age groups and across all subjects (Swanson et al., 1999).

Where can teachers get more information?

Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51(4), 355-368. http://dx.doi.org/10.1037/0003-066X.51.4.355

Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. Washington, D.C.: National Academy Press. ISBN 0-309-07036-8

Carver, S. M. & Klahr, D. (Eds.). (2001). Cognition and instruction: Twenty-five years of progress. Mahwah, NJ: Erlbaum.

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Practice for knowledge acquisition (not drill and kill)

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Researchers who have investigated expert and novice performance have uncovered important distinctions between deliberate practice and other activities, such as work, play and rote repetition.

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Strategies for Metacognition:
- Preassessments
- The Muddiest Point
- Retrospective Postassessments
- Reflective Journals

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In simple terms, metacognition is thinking about one's thinking. In an educational context, it is students' awareness and understanding of their own strengths and weaknesses as learners and their ability to adapt accordingly to be more successful learners. Metacognition involves active control over the cognitive processes engaged in learning. Novices tend to think more like experts when they recognize their strengths and weaknesses as learners. Self-monitoring and self-regulation, important metacognitive processes, lead to better goal-setting, planning, implementation of plans, monitoring of progress toward goals and, generally, better adaptation of learning strategies. Metacognitive practices increase students' ability to determine how to expand their knowledge and transfer that knowledge to new contexts. Recent studies show that these practices can be taught and learned. Students should understand that their ability to be self-regulated learners can improve with practice. Metacongnition is applicable to a single class session, a homework assignment, an exam, or an entire course.

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L2L Strategy - Metacognition | Arizona Site

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In simple terms, metacognition is thinking about one's thinking. In an educational context, it is students' awareness and understanding of their own strengths and weaknesses as learners and their ability to adapt accordingly to be more successful learners. Metacognition involves active control over the cognitive processes engaged in learning.

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Is the Educational Technology Revolution Losing Steam? What Academic Leaders Can Do to Keep Us Moving Forward

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Is the Educational Technology Revolution Losing Steam? What Academic Leaders Can Do to Keep Us Moving Forward

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"Low-stakes, frequent tests are an efficient way to build memory for course material."

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Learning from PowerPoint: Is it Time for Teachers to Move On?

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Learning from PowerPoint: Is it Time for Teachers to Move On?

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"Active involvement trumps passive viewing. Students need to grapple with challenging problems, practice skills, discuss and defend viewpoints. But for this kind of active learning to happen, instructors need to ensure they do not rely too heavily on slides."

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How well do students learn when a lesson is mainly in PowerPoint? Henrik Berger Jørgensen, CC BY-NC-ND

For a brief period in the history of teaching, using PowerPoint automatically qualified you as a tech-savvy professor - an innovator who wouldn't settle for the usual combination of staticky black-and-white overhead films and hand-scrawled chalkboard notes.

Now, it's hard to believe that PowerPoint was once considered innovative by anyone. Popular criticism includes everything from tongue-in-cheek comments about death by PowerPoint to serious claims that it fundamentally degrades how we think and communicate.

But much of today's college instruction isn't in face-to-face classrooms, a setting in which PowerPoint was traditionally used. It's in the burgeoning field of online learning.

So if more learning is moving online, does that mean that teaching with PowerPoint is becoming a thing of the past?

Surprisingly, the answer is no.

Passive learning through PowerPoint?

Even though there's little research that directly addresses whether PowerPoint affects learning in college students, critics have questioned its value in educational settings.

Some ask whether PowerPoint might indirectly undermine the quality of teaching by reinforcing a passive learning approach.

We know that lecturing is less effective than alternative methods. It therefore makes sense for teachers in face-to-face classrooms to question how much of their class time ought to be spent on slideshows.

But the fact is "slideshows" remain a popular method for presenting content in today's online courses.

Technically, these are often not PowerPoints, but decks generated using other types of specialized programs.

And they may differ from standard in-class PowerPoint presentations in important ways.

For example, taking advantage of the increased flexibility of the online environment, they give students more control over how quickly to go through the material and when to backtrack. They can also have more interactive features, such as quizzes, that ask students to apply material while they are learning it.

Even so, the basic - and flawed - idea is the same: put the material in front of students, and learning will happen.

What's wrong with slideshows in online courses

As a psychologist specializing in teaching techniques and course design, when I talk to faculty about teaching effectively with technology, I sometimes tell them to follow the ABS principle: anything but slideshows.

I'm only half-kidding with that blanket statement.

After all, we learn with the same brains in online environments as we do face-to-face; the principles of learning don't change just because the medium changes. And today's learning theorists agree: active involvement trumps passive viewing.

Students need to grapple with challenging problems, practice skills, discuss and defend viewpoints. But for this kind of active learning to happen, instructors need to ensure they do not rely too heavily on slides.

There are alternatives: simulations, problem-based learning, even educational games are all proven methods for drawing students in. They also transplant well into online learning.

Using slideshows the right way

So do slideshows have any place in a well-designed online course? Possibly.

They can be used strategically for things they are best at: giving students an overview of new material, or providing a refresher on concepts students will need for an upcoming activity.

Slides are also great for for integrating visual illustrations. This is important because visuals - diagrams, figures, photos and the like - have a powerful impact on learning.

Visual information is almost always more memorable than sound, text or other modalities.

This isn't because of the now-debunked idea that some people are "visual learners," but more likely stems from how the brain codes images. There are separate systems for representing verbal and visual information in the brain. When we save information in both places, it is easier to recall.

Teachers don't have to stick with static images, either. Even basic animations can illuminate conceptual relationships - such as cause and effect, or the unfolding of a process over time - in ways that text can't.

Furthermore, as University of California, Santa Barbara researcher Richard Mayer has discovered, visuals and the spoken word pair up in powerful ways, so that audio plus visuals produce better learning than either alone.

Research also tells teachers some things not to do with visuals. Instructors should avoid purely decorative graphics, as these can actually hamper learning.

They should also eschew reading text verbatim, instead using a conversational, natural speaking style for voiceovers and verbal explanations.

What this means in the larger context of online learning

In sum, slideshows can be a useful part of online instruction, when used for the right things and designed in the right way. But they shouldn't be the main, or the only, method of instruction - any more than lectures should dominate face-to-face classes.

But it's not just instructors who need to hear this message.

Publishing and educational technology companies, who provide many of the tools that educators rely on, can do more to develop products that push beyond familiar formulas and draw on the latest learning science.

We need tools that make it easy to create assignments that ask students to apply what they have learned, in scenarios that are as realistic and challenging as possible.

These learning tools also need to be adaptive, adjusting the material and pace to the individual learner. This kind of educational technology does exist, but far more can be done to expand the available options.

Teaching in the age of technology comes with its own set of opportunities as well as challenges. And online education presents educators and tech developers with a rare opportunity to fundamentally rethink what we do.

Will we use it to explore new avenues for learning, or will we fall back on the the same old techniques that don't work well in face-to-face classrooms?

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Learning from PowerPoint: is it time for teachers to move on?

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Slideshows, when designed right, can be a useful part of online instruction. But they shouldn't be the main, or the only, method of instruction.

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Helping Students Memorize: Tips from Cognitive Science

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Helping Students Memorize: Tips from Cognitive Science

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- Emphasize context and purpose
- Break down new vocabulary words
- Visualize information
- Take advantage of testing, spacing, and interleaving
- Avoid the rereading trap

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Faculty Focus | Higher Ed Teaching & Learning

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I was wrapping up a presentation on memory and learning when a colleague asked, "How do we help students learn in courses where there's a lot of memorization?" He explained that he taught introductory-level human anatomy, and although the course wasn't all memorization, it did challenge students' capacity to retain dozens of new terms and concepts.

The question itself is tricky because most teaching professionals are heavily invested in the idea that learning isn't about being able to regurgitate facts on an exam. We also worry, and with good reason, that emphasizing rote learning steals time and effort away from the deeper thinking that we want students to do. But in my work I've come to realize that memorization deserves some airtime because it is one important route to building content knowledge and expertise. Furthermore, acquiring content knowledge doesn't have to detract from critical thinking, reasoning, or innovation-rather, it can complement all these. Cognitive scientists are making new discoveries all the time about the connections between memory and processes such as drawing inferences, making predictions, and other skills that make up the ability to think like an expert in a discipline.

So what techniques target this specific teaching and learning challenge? We're all familiar with mnemonics such as the first-letter technique-using a word such as RICE (rest, ice, compression, elevation) or a catchy phrase (Every Good Boy Does Fine) to remember a sequence of information. Mnemonics are one good tool, but what else can we offer?

Learning isn't about being able to regurgitate facts on an exam, but memorization deserves some airtime because it is one important route to building content knowledge and expertise

For starters, we should convey to students (and keep in mind ourselves) that memorization challenges nearly everyone because the mind simply isn't set up to take in reams of disjointed facts. Our memory systems evolved to be picky about what we remember, selecting the information that is most relevant to our goals and discarding the rest. Understanding this helps us and our students have realistic expectations about the level of effort needed to succeed.

It also helps to understand how we create memories for words, because many memorization tasks involve scientific terms, foreign language vocabulary, and the like. How we do this has been hotly debated among researchers, but one view is that we rely on a special piece of mental machinery called the phonological loop that holds speech sounds.

If you've ever muttered a phone number to yourself while you ran around looking for a pencil to write it down with, you've experienced the phonological loop in action. But recycling phone numbers isn't the phonological loop's main reason for existing. Rather, its real job kicks in when we run into a word we haven't heard before. When this happens, the phonological loop grabs onto the sequence of sounds and keeps it fresh while other memory systems set up representations of what the new word means. The catch is that phonological loop capacity varies fairly substantially from individual to individual. This means that words of three, four, or more syllables overwhelm some people's ability to learn new words on the fly.

With these two points in mind-that memory doesn't handle disjointed facts well and new vocabulary hinges on one sometimes-shaky mechanism within memory-here are some ways to help students manage memorization:

Emphasize context and purpose. Ask yourself why students are being asked to memorize these facts in the first place. If students can readily answer that question and if they can picture future situations in which they will use the information, they will be better primed to remember it.

Break down new vocabulary words, especially those that are more than two syllables long. Allow time to rehearse and remember the first couple of syllables before tacking on later ones. Also keep in mind that this process will be easier for some students than others due to wide variation in phonological loop capacity; you may want to design in a "mastery learning" or other individualized approach for vocabulary so that students can move through at their own pace.

Visualize information. For most people, imagery is highly memorable, perhaps because so much of the brain is devoted to visual processing. Memory champions, such as the ones populating the best seller Moonwalking with Einstein, use elaborate visualization strategies to achieve incredible feats of memorization. Similarly, strategies such as the keyword mnemonic work by linking word sounds to images (such as using an image of a cowboy on a horse to remember the Spanish word "caballo," which sounds a bit like "cowboy").

Take advantage of the "Big Three" applied memory principles-testing, spacing, and interleaving. Briefly, these refer to the facts that quizzes are a great way to study and that we do best when we spread out our study sessions and alternate between different topics. Tackling a big memory project such as human anatomy means we need every advantage we can get, and dozens of research studies have supported these three as producing the biggest memory payoff for the time invested.

Avoid the rereading trap. Students tend to fall into passively reviewing material, and in doing so they miss the key advantage of techniques such as testing: retrieval practice. Retrieval practice strengthens memory, but it works only when we actively challenge memory. Flash cards, a favorite student strategy, are fine as long as students use them to actively quiz themselves.

It's okay to expect students to learn the facts and terms needed to become experts in a discipline. With an understanding of memory and some strategies to share, we can make this side of learning productive and maybe even painless-but certainly at least less painful.
Michelle Miller is a professor in the Department of Psychological Sciences at Northern Arizona University.
Reprinted from The Teaching Professor, 28.9 (2014): 3. © Magna Publications. All rights reserved.

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Helping Students Memorize: Tips from Cognitive Science

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Although there is more to learning than regurgitating facts on an exam, memorization is an important route to building content knowledge and expertise.

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The Surprising Habits of Original Thinkers

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The Surprising Habits of Original Thinkers

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Original thinkers are procrastinators, feel doubts and fears, and have lots of bad ideas!

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The surprising habits of original thinkers

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How do creative people come up with great ideas? Organizational psychologist Adam Grant studies “originals”: thinkers who dream up new ideas and take action to put them into the world. In this talk, learn three unexpected habits of originals -- including embracing failure. “The greatest originals are the ones who fail the most, because they’re the ones who try the most,” Grant says. “You need a lot of bad ideas in order to get a few good ones.”

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Free resources for various disciplines and levels

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Problem Library: 116

Crossed Circuits
Two roommates argue about each others use of energy. Which roommate should pay a utility premium? How much extra?

Watson, George

Physics and Astronomy

1 Hour or Less

Introductory (non-majors)

What Is the Cause of This Swelling and Why Am I Short of Breath When I Exercise?
"Joan is a corporate lawyer for a Fortune-500 company. She has been physically active her entire life.
Lately Joan has. . .

Tallitsch, Robert

Biological Sciences

1 Hour or Less

Intermediate (majors and non-majors)

The Fastest Man in the World
This problem introduces students to speed and acceleration.

Duch, Barbara

Physics and Astronomy

3-6 hours

Introductory (non-majors)

Pass the Buffer-in
A student needing to prepare a pH 7.5 buffer must decide whether an unlabeled recipe will do the job.

Groh, Susan

Chemistry

1-3 Hours

Introductory (majors and non-majors)

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DISCIPLINEAdd Filter "Collapse " Biological Sciences Biotechnology Chemistry Accounting Aviation Science Business Administration Chemistry and Bi...

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Teaching for Critical Thinking: Helping College Students Develop the Skills and Dispositions of a Critical Thinker

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Teaching for Critical Thinking: Helping College Students Develop the Skills and Dispositions of a Critical Thinker

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"The changing nature of technology has not only provided us with more and better ways to teach in general but has also increased the need for the skills of critical thinking."

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Can Millennials Pay Attention to Classwork While Texting, Tweeting and Being on Facebook?

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Can Millennials Pay Attention to Classwork While Texting, Tweeting and Being on Facebook?

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"College teachers need to include lots of tech support for online assignments and other kinds of educational technologies, because even students who have grown up with computers still get stuck when using them in new contexts. Teachers must also avoid the trap of adding tech to a class just because they assume digital native students want it. Educational technology can be highly effective, but only when it is tightly coupled to the teacher's goals."

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Should mobile devices be encouraged in class? Trinity College, CC BY-NC-ND

It's hard not to notice the connection of today's youth to technology.

Fused to their smartphones around the clock, they prefer screens to paper and text message to speech; they consider leaving voicemail an act of interpersonal aggression.

They seem to focus differently too: skimming and sampling their way through multiple streams of data, they look like they're taking it in all at once.

Some educators call them "digital natives," reflecting the idea that tech is at the core of who they are and how they function.

If living with technology really has rewired this generation for multitasking, what implications does this have for how we educate them? Should we tolerate - or maybe even encourage - mobile devices in class? And should we worry when we see students keeping an eye on social media or other diversions while doing homework?

Why attention matters for learning

As a professor who specializes in course design, I deal with these questions frequently as I help fellow faculty devise better strategies. In this work, I draw on my research background in cognitive psychology, a specialization focusing on mental processes such as reasoning, memory and attention.

Of those processes, attention is one that I tell teachers to be particularly attuned to. Research shows that memory - especially working memory, which holds information we're using in the present moment - is deeply intertwined with attention.

Without sustained focus, we retain surprisingly little, and that window of focus is much narrower than we may realize.

So, are the minds of digital natives - or any heavy tech users - better or worse when it comes to attention? It's a complicated question, partly because attention works in some paradoxical ways.

The function of attention is to prioritize where we put our limited cognitive resources at any given moment. One thing attention does is keep irrelevant information at bay. Like a bouncer at an exclusive nightclub, its job is to ensure that only the most important and relevant elements pass through into conscious awareness.

But at the same time, a well-functioning attention system has to stay open to information that's in the background and could be potentially useful. That mental bouncer has to constantly scan the crowd for anyone who might turn out to be a great addition to the party, pick them out and usher them inside.

What technology does to the ability to pay attention

Technology seems to have a bigger impact on that second side of attention.

Certain kinds of tech use - habitually consuming lots of online media at once, or playing certain kinds of video games - appear to boost the ability to efficiently pick up on peripheral information while keeping up with a main task.

And although long-term studies are lacking, there is a growing body of research on how tech-saturated environments shape the minds and brains of kids.

It's unlikely that video games or online media damage kids' ability to pay attention. But comparative research across high-tech and low-tech societies suggests that information processing is different in kids who grow up working with digital rather than physical tools. Neither group is better or worse across the board, but this research suggests that high-tech kids may be less inclined to learn by watching for extended periods of time.

That said, we should be cautious about concluding that today's students have developed the ability to juggle as much technology as they want. Technology has not reshaped the basic ways in which our brains process information.

As cognitive psychologist Daniel Willingham points out, the impact of things like video games is relatively subtle. All they can do is tinker around the edges of our mental systems, rather than altering them at the core.

As Willingham says, something as fundamental as attention couldn't be deeply reshaped without a major overhaul of the brain - something that would be a function of evolution, not life experiences.

All of this means that college teachers should be skeptical of claims that their game- and tech-obsessed students process information completely differently. Compared to earlier generations, today's students are probably no more able to learn while simultaneously engrossed in Twitter, Facebook and texting.

College students and technology

There are other pitfalls to consider.

Research with young adults in college suggests that they are neither as enthusiastic about technology nor as adept at using it as we may assume. Sometimes, they intentionally opt for lower-tech approaches; in one study of California college students, most said that they preferred paper over a browser for their own studying.

This is something my faculty colleagues commonly observe as well - that although their students may use technology like social media casually throughout the day, it doesn't translate to other tech-based tasks, such as navigating a course's online homework system.

Students also get into trouble if they assume - because of the "digital nativism" idea, or simple lack of self-awareness - that they can master demanding coursework while engaged in digital distractions.

Evidence suggests that multitasking - such as tweeting in class - reduces learning, or at the very least makes it take longer.

Even sitting next to a classmate surfing the web on his or her own laptop hampers learning.

In sum, despite that appearance of being fused to their devices, today's students aren't immune to the distraction those devices cause. And they don't necessarily want technology in every corner of their educational lives.

How college teachers can help

College teachers need to include lots of tech support for online assignments and other kinds of educational technologies, because even students who have grown up with computers still get stuck when using them in new contexts.

Teachers must also avoid the trap of adding tech to a class just because they assume digital native students want it. Educational technology can be highly effective, but only when it is tightly coupled to the teacher's goals.

Even better, teachers can help students understand for themselves how attention works - knowledge that everyone ought to have in today's distracting era. Without vilifying technology, teachers can work to raise students' awareness about how attention impacts learning more than we may realize, no matter what our age.

The effects of technology on cognition are intriguing, but they don't justify teaching millennials as if they were a new species. Teachers need to think twice about tolerating devices in class, except as part of structured activities that link to the lesson at hand.

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Can millennials pay attention to classwork while texting, tweeting and being on Facebook?

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From the look of it, the millennials appear to be very comfortable with technology. But are they as immune to the effects of digital distraction as some might assume - especially in the classroom?

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What College Teachers Should Know About Memory

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What College Teachers Should Know About Memory

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"For reasons that have now been thoroughly explored from both theoretical and practical perspectives, students should engage with material frequently and in ways that require them to retrieve material from memory."

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"Over the past decade, neuroscience has disproven several common misconceptions through neuroimaging devices and other brain research."

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September 2017 - Powered by PageTurnPro.com

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Mastery isn't just about the amount of hours you practice but the quality and effectiveness of your practice.

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How to practice effectively...for just about anything - Annie Bosler and Don Greene

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"Emotions or light to moderate forms of stress (i.e., cognitive challenges without excessive demands or moderate emotional arousal that results, e.g.,. from hearing something that is unexpected) may increase memory formation, which may have positive effects on memories for study material. Moreover, stress may lead to stronger memories for negative events happening in the classroom, such as failed exams, embarrassing experiences or interpersonal conflicts (e.g., bullying) and these strong negative memories may induce long-lasting frustration and a negative attitude towards school and the individual's abilities."

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Learning and memory under stress: implications for the classroom - npj Science of Learning

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npj Science of Learning - Learning and memory under stress: implications for the classroom

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Engagement, Representation, Action & Expression

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The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a framework to improve and optimize teaching and learning for all people based on scientific insights into how humans learn. Learn more about the Universal Design for Learning framework from CAST. The UDL Guidelines can be used by educators, curriculum developers, researchers, parents, and anyone else who wants to implement the UDL framework in a learning environment. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

Reenvisioning the UDL Guidelines

Because the UDL Guidelines are meant to be informed by feedback from the field as well as new research, they have been updated several times in the past. We are in the process of updating the Guidelines once again in our UDL Rising to Equity initiative.

This update will focus specifically on addressing systemic barriers that result in inequitable learning opportunities and outcomes. CAST aims to develop a transparent, inclusive, and community-driven process. If you are interested in collaborating and staying updated on our progress, we invite you to complete a brief survey.

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The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a framework to improve and optimize teaching and learning for all people based on scientific insights into how humans learn. Learn more about the Universal Design for Learning framework from CAST. The UDL Guidelines can be used by educators, curriculum developers, researchers, parents, and anyone else who wants to implement the UDL framework in a learning environment. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

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Neuroplasticity, Optimal Learning & Regulations

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Neuroplasticity, Optimal Learning & Regulations

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"Approximately 90% of college-aged students (18-23 years old) reported education as a significant source of stress."

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What is Neuroplasticity and Why is it Important?

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What is Neuroplasticity and Why is it Important?

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5 Benefits of Brain Plasticity:
1. Helps you learn new things
2. Helps with depression
3. Helps you engage in activities more thoughtfully
4. Aids recovery from strokes and other traumatic brain injuries
5. Increases brain volume and memory

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The human brain is one of the most incredible and intricate parts of the human body. In fact, it's capable of creating more ideas than there are atoms of the universe.

Did you know that the brain changes constantly through a process called neuroplasticity?

But, what is neuroplasticity?

Through neuroplasticity, the brain is consistently rewiring itself and modifying its connections. It can reorganize itself both in structure and how it functions.

Without neuroplasticity, we wouldn't be able to do many of the things that make us human. This includes learning, developing, and forming memories.

So if the brain is malleable, can we rewire it?

Can we "rewrite" patterns in our brain to improve well-being?

Here's everything you need to know about how neuroplasticity works. Plus, we'll show you how you can improve your brain's plasticity.

What is neuroplasticity?

Let's start with a plasticity definition.

Neuroplasticity is also known as neural plasticity or brain plasticity. It's the brain's ability to reorganize and restructure itself on a cellular level.

This reorganization allows our brains to adapt to changes. These changes could come from:

New experiences

Environmental changes

Brain damage

Environmental pressure

Physiological changes

New experiences

How does neuroplasticity work?

The human brain consists of vast networks of neurons. These are the single individual working units of its structure.

Even the simplest of tasks require millions of interconnected neurons. These neurons function as connectivity networks to accomplish the action at hand.

The neural network is a unique and specific pattern of neural connections. They fire in equally specific sequences to allow you to accomplish different tasks.

From raising your hand to playing "Chopsticks" on the piano, they are always working. The ability of the brain to adapt is based on the alteration of existing neural networks. And the generation of new ones.

Psychologists have found that there are two primary types of neuroplasticity:

Functional neuroplasticity.This refers to the permanent structural changes in neural synapses. It's brought on by development and learning.

Structural neuroplasticity.This refers to the strength of the connections between neurons and changes to this strength.

Synapses between neurons alter through a process called synaptic pruning. This occurs mostly between early childhood and your mid-20s.

In this phase of nervous system development, synapses are removed.

5 benefits of brain plasticity

Neuroplasticity is beneficial to our cognition in every way.

It shapes our views of the world and how the brain works. It also impacts our:

Memories

Learning abilities

Subconscious beliefs

These are the five significant benefits of improving your brain plasticity.

1. Helps you learn new things

Every time you learn a new skill, you have your neurons to thank for it.

Activity-dependent neural plasticity is a form of functional and structural plasticity. It comes about due to the use of cognitive function and our personal experiences.

Essentially, it's the biological mechanism for learning and creating new memories.

2. Helps with depression

Neuroplasticity deficits are a possible contributor to conditions like anxietyand depression.

Studies show that the brains of people with depression indicate problems with plasticity. Plus, many antidepressants are believed to work because they promote neuroplasticity.

Experts suggest that certain lifestyle changes could cause neuroplasticity enhancement. In turn, this could potentially improve the symptoms of depression. These changes include:

3. Helps you engage in activities more thoughtfully

Mental exercises help create new connections between neurons and generate new brain cells. This develops plasticity.

It builds a functional reserve to guard against future brain cell loss. It also enables you to engage in activities more thoughtfully and consciously.

All mentally stimulating activities can help boost your brainpower and improve focus. This includes everything from Sudoku puzzles and reading to mindful breathing and math problems.

You can engage in activities that require manual dexterity. This includes activities like:

4. Aids recovery from strokes and other traumatic brain injuries

After suffering a traumatic brain injury, neuroplasticity can help you regain brain function.

A neuroscience study found that task-specific activities promoted stroke patients' recovery. It encouraged increased neurogenesis and neural plasticity.

5. Increases brain volume and memory

Improving your neuronal plasticity also boosts neurogenesis. This is the formation of new cells in your brain. This can boost your brain's volume considerably.

A high rate of neural plasticity strengthens the connections between these new neurons. This can further enhance your memory and learning abilities.

Key characteristics of brain plasticity

It was once thought that as we age, our brains' neural networks become fixed and rigid. But recent research shows that our brains never stop adjusting and changing.

Younger people are generally more sensitive to changes in their plasticity. But even seniors can learn new things and boost the plasticity of their brains as well.

If you play an instrument often, you're likely to become better at playing that instrument. This is due to your constant use of those neural networks.

Likewise, if you were to stop playing your instrument for a while, and you take it up again in a few months, you might find your playing rusty.

Although neuroplasticity is a promising scientific field, it does have its limitations. Brain plasticity cannot fix everything. Unfortunately, it cannot fix brain damage in severe cases.

For example, most of the evidence of neuroplasticity is around the cerebral cortex. When this area is damaged, other areas of the brain can often compensate for the loss.

However, the cortex cannot replicate the functions of more complex brain regions like the hippocampus.

Neuroplasticity is an ongoing process. It's hindered or promoted by factors like:

Practices such as neuroplasticity training can promote ongoing brain plasticity. They increase your brain's formation of strong synapses between neurons.

Neuroplasticity and psychology

People who undergo therapy need to learn vital new coping skills to handle life's challenges. As they do, they build new neural connections that promote lasting emotional regulation and resilience.

As you learn new habits, your synapses will replace the neural pathways that prompted unhealthy behaviors.

Once these synapses are removed, your counseling will rewire your brain. New coping mechanisms that you need to practice to turn into a habit will emerge.

Conditions such as depression and chronic stress have been associated with suppressed neurogenesis. Psychiatry experts believe that promoting this process could reduce their symptoms. It could even prevent the conditions in some cases.

Certain psychiatric medications can also improve neuroplasticity.

The reasons behind the efficacy of antidepressants were long thought to be a mystery. But today, neuroscientists know that they are at least in part based on brain plasticity.

SSRI and SNRI antidepressants often boost neuroplasticity. They may even reverse some of the negative effects inflicted on the brain during a depressive episode.

Often, boosting neuroplasticity can form part of a mental health treatment regime. Brains in the first stages of dementia use neuroplasticity to compensate for rapid cognitive declines.

Does neuroplasticity change with age?

As kids, our brains are more capable of change. Children are constantly learning and experiencing new things. This creates extraordinary changes in their brain's structure.

At birth, a baby's brain has 100 billion neurons. As the neurons mature, more synapses are made. When we're born, there are 2,500 synapses per neuron. But by age two or three, it's about 15,000 per neuron.

That's more than double the number of synapses of the average adult brain.

This explosion of synapse formation is called synaptogenesis. It plays a crucial part in early brain development.

Neuroplasticity continues to happen throughout our lifetime. But different types of plasticity are more prevalent during certain periods.

There are four main types of neuroplasticity observed in children:

Adaptive plasticity.This happens when children practice a skill or learn new information. It enhances skill development and recovery from brain injury.

Impaired plasticity.This type of neuroplasticity happens due to an acquired or genetic disorder. It's associated with cognitive impairment.

Excessive plasticity.Also known as maladaptive plasticity, this process involves the reorganization of maladaptive pathways. This can cause disorders or disabilities. The best example of maladaptive plasticity isfocal dystonia. It is a rare neurological disorder that involves involuntary spasms in small muscles in the body.

Injury vulnerability plasticity.This type of neuroplasticity involves the formation of harmful pathways in the brain. This makes the brain more vulnerable to injury.

As we age, the brain starts to remove synapses that it no longer needs. Old connections are deleted through synaptic pruning. This happens when we are about 2 to 3 years old, after the number of synapses reaches a peak level.

Our brain's plasticity is at its highest during the first 20 years of life. What's more, we are constantly losing neurons as we age.

But the adult brain can still create new neuronal connections. Neuroplasticity is not only prevalent in children.

With the right lifestyle, adults can increase their brain's plasticity.

How to rewire your brain with neuroplasticity training

Neuroplasticity training includes lifestyle choices that promote the regeneration of nerve cells. With regular practice, it helps to improve:

Let's look at five ways you can promote neuroplasticity.

1. Utilize brain games

Brain training exercises create targeted mental stimulation and activation. This reorganizes the connections between neurons and improves brain plasticity.

A study found that younger adults who enjoyed brain training games had better executive function. They also had improved memory, self-control, and brain-processing speed than their peers.

Nowadays, you can easily use apps like CogniFit and BrainHQ for mental stimulation.

2. Rest when you need to

A good night's rest helps to create lasting memories. It also generates synapses to store skills and information that you learned in the day.

3. Exercise regularly

Research shows that daily exercise is essential for focus, concentration, and mood. It's recommended that you get in 150 minutes of moderate-intensity exercise each week.

Exercise boosts the density and size of cortical neurons. This can benefit:

Attention

Perception

Memory

There's even some evidence that it can delay Alzheimer's disease.

Physical activity also helps to decrease inflammation and improve your quality of sleep. Both of which are factors that can impact your cognitive function.

4. Adopt a healthy diet

Nutrition plays a role in so many areas of our lives, and it's important for brain health too. You can promote neuroplasticity by eating a healthful, anti-inflammatory diet. This should include plenty of essential antioxidants and brain-fuelling omega-3 fats.

Improving the health of your gut microbiome can also promote neuroplasticity.

Eat unprocessed foods with prebiotic fibers to feed the good bacteria in your gut. Consider adding fermented foods such as kimchi to your daily menu.

5. Connect with others

Positive social experiences prime our neural connections to make meaningful connections with others.

The more we make an effort to connect with and care for others, the stronger these impulses will become. And the more naturally we'll find ourselves gravitating toward our peers.

History and research of neuroplasticity

Let's take a look at both early and modern theories surrounding the concept of neuroplasticity.

Early theories

The idea that the brain is not fixed was first proposed by William James in 1890. Later in the early 1900s, the father of neuroscience, Santiago Ramón y Cajal, used the term 'neuronal plasticity.' This referred to non-pathological changes in the structure of adult human brains.

In 1923, Karl Lashley performed experiments on rhesus monkeys. He noted changes in their neural pathways, which he believed was evidence of plasticity.

Then in 1945, Justo Gonzalo theorized that the central cortical mass was a 'maneuvering mass.' It could reorganize its functions with its neuroplastic properties.

Modern theories

One of the key founders of the modern field of neuroplasticity was Dr. Michael Merzenich. In the early 1970s, Merzenich conducted experiments to prove that the brain was fixed. He tried to prove that the brain was not capable of structural or functional change.

Interestingly, he proved his own theories incorrect. He discovered that the brain and its neurons are plastic. Lost skills stored in damaged tissue could be relearned in other parts of the brain.

Merzenich also found that the human brain has a 'cortical map' of the physical parts of the body. If one map was deprived of its information inputs, it could become active again in the future if other maps close to it were stimulated.

Merzenich suggested that neuroplasticity could happen beyond developmental periods and into adulthood.

In a study with Clinton Woosley, Merzenich tried to observe what happened in the brain when one peripheral nerve was severed and then regenerated. The pair micro-mapped the hand maps of monkeys' brains before and after severing the nerves and reattaching them.

They found that the brain maps could normalize their structure, even in response to abnormal inputs. This proved that the brain had to be plastic in order to reach this degree in compensation.

Merzenich was awarded the 2016 Kavli Prize in Neuroscience for his work in discovering adult brain plasticity.

Healing the brain with neuroplasticity after trauma

People with varying degrees of brain trauma can recover full functionality thanks to neuroplasticity. Based on research published in Translational Research in Traumatic Brain Injury, the brain goes through three main phases of neuroplasticity after experiencing trauma:

Phase 1

Directly after the injury, neurons start to die. Cortical inhibitory pathways are decreased. This initial phase lasts 24-48 hours.

It may stimulate secondary pathways that had rarely been used before the trauma.

Phase 2

The second phase takes place a few days after the trauma. The activity of these cortical pathways changes from inhibitory to excitatory.

New synapses are created. Neurons and other brain cells replace the damaged cells to facilitate healing.

Phase 3

The final phase takes place after a few weeks. The new synapses continue to be generated to remodel the brain.

During this phase, therapy and rehabilitation can help the brain learn new neural pathways. This allows for the brain to reduce the effects of the trauma.

There are several medical treatments in development that encourage neuroplasticity after brain injury. There are also therapies involving stem cellsand gene expression being studied for their potential to treat trauma.

Neuroplasticity rehabilitation for stroke recovery

A stroke causes damage by cutting off the flow of blood to certain parts of the brain. This deprives the brain of oxygen and essential nutrients.

Depending on the severity of the stroke, volumes of brain cells may die. Brain volume may be reduced, and dysfunction may occur as a result. The recovery process after a stroke relies on the brain's ability to heal itself.

The brain works around the dead brain cells. It attempts to build other neural pathways to compensate for lost function.

Physical, occupational, and speech therapies are all designed to promote neuroplasticity. They encourage the brain to overcome any mental and physical deficits.

It's important to begin the rehabilitation process as soon as possible after a stroke. This will make use of the brain's natural increase in neural plasticity after the trauma.

Stroke victims can promote recovery by participating in therapies that:

Focus on massed practice. This is the consistent repetition of a movement or skill that promotes fast learning.

Include physical exercises and therapies to improve patient mobility and strength.

Reteach them the skills they will need to go about their daily lives. This includes standing up, basic personal hygiene, simple cooking, andcommunication skills.

Include speech therapies. This will enable patients to practice their language skills while stimulating their brains.

How can neuroplasticity help with anxiety and depression?

Depression and anxiety have both been linked to a decrease in neural plasticity in the brain.

Modern therapies are aimed at encouraging neuroplasticity. This is so that patients can learn valuable coping skills for life's many challenges.

Neuropsychiatry's understanding of neuroplasticity can be summed up as follows:

Our daily behaviors can have remarkable effects on the structures of our brains. Anxiety and depression can, at least to a degree, be "unlearned." Their tendencies can be replaced with more constructive pathways through professional neural training.

COVID-19 PTSD is set to become a significant public health concern in the future. Prolonged exposure to stress and anxiety can lead to PTSD. But therapies focusing on promoting neural plasticity may induce positive plasticity.

This could help combat these conditions before they have a chance to develop into a disorder.

Simple neuroplasticity exercises can all promote neural plasticity. These include:

Regular exercise

Healthy diet

Connecting with your loved ones

They also help reduce the symptoms of anxiety and depression.

Experts recommend activities like learning a new language or skill. You could also perform a manual motor activity like painting or building a model. Or you can try playing brain-training games to promote neuroplasticity training.

The power to change your mind

Neuroplasticity and synaptic plasticity are intrinsic in every facet of our lives. Our brains are constantly performing synaptic pruning and making new neural connections. This is true regardless of what we happen to be doing at any given time. Or even whether we are awake or asleep.

Improving your brain's plasticity will sharpen your memory, concentration, and cognitive skills. Plus, it can help you address depression and anxiety. It may even promote healing in the case of adult brain trauma or injury.

BetterUp Coaches can help you understand the amazing ability of your brain to constantly learn. Coaching can guide you with how to put plasticity to work to your advantage to change behaviors and show up at your best. Use your brain's innate healing abilities to perform better and unlock more of your human potential.

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What is Neuroplasticity and Why is It Important?

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The human brain is constantly building new connections using its neuroplasticity. Read on to learn about what neuroplasticity is and how it benefits you.

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Growing Brains, Nurturing Minds-Neuroscience as an Educational Tool to Support Students' Development as Life-Long Learners

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Growing Brains, Nurturing Minds-Neuroscience as an Educational Tool to Support Students' Development as Life-Long Learners

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"Learners cannot be passive recipients of content but must be active participants in the learning process."

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Department of Psychology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

1. Educational Neuroscience (Teaching for the Brain and Teaching about the Brain)

Educational neuroscience is an interdisciplinary field exploring the effects of education on the human brain and promotes the translation of research findings to brain-based pedagogies and policies [ 1]. The brain is the target organ of education. Education is thought to influence brain development [ 2, 3] and health, even as the brain ages [ 4, 5]. Studying the dynamics between the brain and education can be instrumental in finding ways to better support learners across the lifespan.

Educational neuroscience research explores every possible relationship between the physiological, mental, and behavioral aspects of learning. Some studies have tried to identify the optimal physical conditions for neuroplasticity and learning. This stream of educational neuroscience research includes studies exploring the effects of sleep (or sleep deprivation), physical exercise, and environmental pollution on the brain and its cognitive performance [ 1]. While these studies focus on the effect of brain health on learning, other studies examine the effect of learning on brain health, assessing the long-term effects of learning/education on the human brain and exploring in what ways formal/informal education is associated with better aging of the human brain [ 2, 3, 4].

Some educational neuroscience studies take a developmental approach to study the relationship between cognitive and learning capacities across the lifespan. For example, multilevel measurements collected from adolescents (e.g., neuronal, hormonal, psychological, and behavioral) have advanced our understanding of how the massive neuronal changes that take place during adolescence promote cognitive development but also introduce immense neuronal and mental vulnerability (and the onset of most psychiatric disorders) [ 1, 5, 6, 7]. Other studies in this line of research explore the factors supporting neuroplasticity in the mature brain-to support lifelong learning [ 8].

Educational neuroscience also explores the nature-nurture aspects of learning, for example, examining how learning environments interact with genetic conditions and what DNA variations predict differential learning abilities [ 9]. Environmental influences on learning include studies about the impacts of socio-economic status (SES) on the brain and cognitive developmental trajectory [ 10]. Furthermore, educational neuroscience seeks to understand the mechanisms that facilitate general learning abilities (such as executive control and social and emotional skills), discipline-specific learning abilities (such as literacy, numeracy, and science), the connections between these mechanisms, and the extent to which these learning skills are trainable [ 11].

As a developing, interdisciplinary research field, educational neuroscience faces challenges, limitations, and criticism, especially concerning the ability to generalize research findings in lab conditions to classroom learning, and its validity and transferability to larger scales, such as mass education systems. Other challenges stem from the fact that learning is one of the most basic yet complex brain functions that incorporates the entire brain and has a continuous effect. Furthermore, empirical studies in educational neuroscience are challenging and cumbersome due to the interdisciplinary nature of the field (education, psychology, and neuroscience); the need for repeated measures over time; and the young target population (school students), which imposes ethical restrictions on experimental designs. Finally, while still evolving as a research field, educational neuroscience is intriguing for many educational leaders who are enthusiastic about applying neuroscience in education practices. Unfortunately, the current gap between the high demand and limited supply may lead to misuse of neuroscience in pedagogy (e.g., neuromyths or the justification of educational methods based on limited to no evidence) [ 1].

While educational neuroscience is preliminary in forming evidence-based pedagogy, it can already offer valuable information and a much-needed bridge between educators and scientists in translating the research of learning into effective educational practices.

Neuroscience-informed educational design (teaching the way the brain learns) can promote learning motivation, high-level information processing, and knowledge retention. Moreover, neuroscience educational content (teaching about the brain) can inform students about their developing brains to promote scientific education and self-exploration.

1.1. Learning and Neuroplasticity

Human development is based on nature (genetics), nurture (physical and social environments), and their interactions (epigenetics) [ 12, 13]. These factors play an essential role in learning processes and the reorganization of neuronal networks to create neuronal representations of new knowledge. Learning and training new knowledge or skills evoke specific and repeated activity patterns, and in the process of Hebbian neuroplasticity, neural pathways are reinforced by the strengthening of specific synapses, while less functional ones are eliminated [ 14, 15, 16].

Almost half a century ago, Vygotsky introduced the zone of proximal development (ZPD) [ 17] in education. According to the ZPD, learning and development depend on an optimal balance between support and challenge (see Figure 1: the zone of proximal development and neuroplasticity), which should be tuned and tailored for each learner based on their specific developmental stage. The ZPD model was revolutionary, as it emphasized the importance of the educational environment (nurture) in unlocking the internal potential (nature) of students, and it placed the learning process (as opposed to the learning product) as the central educational goal [ 17]. Some decades later, the biology of learning revealed a beautiful alignment with Vygotsky's theory-with evidence showing that brain neuroplasticity is highly affected by environmental conditions and the balance between demands (challenge) and available resources (support) [ 18]. The impact of stressors on learning can be constructive or destructive depending on the intensity, duration, and accumulation of the stressors and the coping mechanisms and support that one has.

Neuroscience research suggests that experience-dependent neuroplasticity [ 19], which facilitates learning processes, benefits from several principles. The central one is that learning a skill or new knowledge requires the activation of relevant neuronal pathways. The research also points to the saliency, intensity, and repetition of the learned skill/knowledge as valuable strategies for enhancing neuroplastic changes [ 16, 20, 21]. Learners cannot be passive recipients of content but must be active participants in the learning process.

An enriched environment for enhanced neuroplasticity offers physiological integrity, cognitive challenge, and emotional safety. More specifically, an enriched environment includes adequate sleep and nutrition, sensory-motor and cognitive challenges, opportunities for exploration and novelty, and secured relationships that act like a safety net and enable learners to take on challenges [ 22, 23]. Conversely, a lack of these conditions may slow down or decrease the level of neuroplasticity in the developing brain.

The social and cognitive safety net that enables learners to aim high while taking risks and to turn failure into resilience is rooted in safe relationships (with adults and peers) and in holding a growth mindset. A growth mindset is the belief that intelligence and learning potential are not fixed and can be developed [ 24]. Holding a growth mindset has been associated with academic success, emotional wellbeing, and motivation while reducing racial, gender, and social class achievement gaps [ 25, 26, 27, 28, 29, 30]. While the impact of mindset interventions on academic performance is debatable regarding the general population [ 31], the literature is clear about the potential of growth mindset intervention in supporting the academic development of high-risk and economically disadvantaged students [ 26, 27, 31, 32].

The notion of human potential as something dynamic resonates with the concept of the plastic brain. Moreover, teaching students about neuroplasticity and the dynamic potential of their brains has been shown to effectively reinforce a growth mindset [ 32].

1.1.1. Using Neuroplasticity as Educational Content

Teaching students about experience-based neuroplasticity and the dynamic changes in neuronal networks during learning provides strong evidence of their natural and powerful learning capacity. Furthermore, teaching students about neuroplasticity with explicit connections to the growth mindset and development creates a motivating premise for learners-according to which their learning potential is dynamic and depends significantly on their attitudes and learning practices.

The neuroplasticity rules of "use it or lose it" and "use it to improve it" mean that, while teachers should support and guide them, learning occurs by and within the students. This physiology-based realization can help build students' responsibility and ownership over their learning.

Harnessing neuroplasticity and a growth mindset to motivate students can be especially important with neurodivergent learners, whose cognitive development and learning styles deviate from the typical range. Twenty percent of the population is neurodivergent, including students on the autistic spectrum (ASD), students with learning disabilities (e.g., dyslexia), attention disorders (e.g., ADHD), neurological disorders (e.g., epilepsy), and mental illness (e.g., PTSD). While neurodiversity and variations in neuronal and cognitive expressions hold many advantages [ 33], neurodivergent students face extra challenges navigating neurotypical-oriented school systems. Learning about neuroplasticity can be a potent form of validation for neurodivergent students, as neurodiversity is a natural result of experience-dependent neuroplasticity [ 19]. In addition, by fostering a growth mindset and neuroplasticity awareness, neurodivergent students can be motivated to participate in evidence-based interventions. For example, teaching students with dyslexia about the specific structural and functional brain changes associated with the reading interventions that they apply [ 34, 35, 36] can motivate them to endure the hard work before noticing visible results.

1.1.2. Using Neuroplasticity to Guide Learning Design

Organizing learning systems around conditions that promote neuroplasticity can enhance learners' academic development and wellbeing. When a student accomplishes today what was not in their reach yesterday, it is the product of neuroplasticity through a growth mindset.

Educational environments that promote neuroplasticity include encouraging and modeling a healthy lifestyle (physical exercise, a balanced diet, sufficient sleep, and regulated stress), -for example, educating students about the counter-productiveness of sleep deprivation (e.g., "all-nighter" study marathons) on learning. In addition, learning systems should invest in intellectual stimulation (novelty and challenge) and the system's social and emotional climate (human connections). Neuroplasticity and development are optimal in the stretch zone, where learners experience a motivating level of challenge and stimulation while feeling emotionally supported and socially safe. This ratio between support and challenge should be individualized (between learners and within learners over time).

Educating teachers about neuroplasticity can be powerful in understanding and supporting students that were affected by trauma. Childhood adversity hampers neuroplasticity duration and magnitude [ 37]; a surviving brain is not a learning brain. While neuroplasticity is compromised by early trauma, neuroplasticity is also the key to healing from trauma. Schools have a pivotal role in battling the damage of early trauma by creating enriched and safe learning environments that reinforce alternative neuronal pathways to reverse the effects of early adverse environments on child brain development [ 22, 38, 39, 40].

1.2. Learning Motivation and Reward

Learning and adaptation are essential for surviving and thriving in dynamic environments. The brain evolved to make sense of information from our external and internal environments and to produce adaptive behaviors that promote survival. The brain is, therefore, a learning machine by nature, and learning does not require external initiation. However, learning is highly experience-dependent and can be directed and enhanced through education.

The brain reward system evolved to reinforce effortful behaviors that are essential for survival (e.g., foraging, reproduction, and caregiving). Such behaviors activate the dopaminergic system associated with reward and motivation [ 41]. The hormone/neurotransmitter dopamine is a central player in reward-motivated behavior and learning through the modulation of striatal and prefrontal functions [ 42]. The human brain reward system balances between (limbic) impulsive desire and (cortical) goal-directed wanting to guide flexible decision-making and adaptive motivational behaviors.

Psychologically, intrinsic motivation is driven by the need to experience a sense of competence, self-determination, and relatedness [ 43, 44, 45, 46, 47].

Competence refers to a perception of self-efficacy and confidence in one's abilities to achieve a valuable outcome. Self-determination refers to the sense of autonomy and agency in the learning process. Relatedness refers to the drive to pursue goals that hold social value, which can be achieved by working collaboratively as part of a team or by creating something that resonates with others. Relatedness is a strong motivational driver, as it touches on a primary and primordial need to be part of a group and a higher spiritual and intellectual need for self-transcendence and impact.

Overall, these components are based on the human inclination to be valued and validated by the self and others. Biologically, they reflect basic survival needs that combine self-reliance (competence and ownership) and social reliance. Psychologically, these are all subjective perceptions that serve the need to maintain positive self-perception and self-integration. Finally, educationally, they reflect the natural human curiosity and tendency to learn and develop continuously.

The human brain reward system in the 21st century is an evolutionary mismatch. There is a discrepancy between the conditions that the reward system evolved to serve and those that it often faces in the 21st century. The reward system evolved over millions of years to motivate humans to work hard (invest time and energy) in maintaining their survival needs (e.g., nutrition, protection, reproduction, and the learning of new skills). However, this system is not designed for the abundance and immediacy of stimulation in the digital and instant reward era, which promotes the persistent release of dopamine that leads to an increased craving for reward (seeking behavior; wanting) and a decreased sense of pleasure and satisfaction (liking) [ 42, 48].

Some of the most significant challenges of modern education systems relate to the massive changes in how people consume information and communicate in the digital era. Digital platforms have become dominant in information consumption and communication, which provide access to unlimited information and reinforce immediate rewards.

1.2.1. Using Neuroscience (of Reward and Motivation) as Educational Content

The science of human motivation, including its evolutionary mismatch, can be utilized to shed some light on students' struggles with learning motivation. It can further provide a framework for students to explore their motivational (approach or avoid) tendencies regarding learning and academic challenges. Moreover, learning the neuroscience underlying motivation and reward can raise students' awareness and proactivity in managing and protecting their reward system. Since adolescence is the peak time for the initiation of substance use, and early onset imposes a higher risk of mental health and substance abuse disorders persisting into adulthood [ 49, 50, 51], neuroscience knowledge about the reward system and its vulnerability (especially during brain development) is essential educational knowledge that can help in the prevention and mitigation of teen addiction.

1.2.2. Using Neuroscience to Guide Learning Design and Intrinsic Motivation

While students of the digital era are the most stimulation-flooded and attention-challenged in human history, learning is a process that takes time, selective attention, and perseverance. Therefore, learning designs that harness students' intrinsic motivation for training and the development of stamina and grit (skills that might be hampered in the digital era) are precious for students' health and success.

Motivational drivers include an adequate level of challenge that fits the student's sense of competence and that creates optimal arousal levels, opportunities to expand social relatedness and impact, and balance between support and autonomy (see the ZPD, Figure 1).

Importantly, in classroom learning, educators are required to manage the attention, motivation, and reward system of not one but many students, which is a complex task. The typical classroom presents a broad spectrum of learners with diverse learning needs and stretch zones ( Figure 1). While the facilitation of autonomy and the sense of competence varies between learners and requires personalized support, the social norms that promote learning are more ubiquitous and apply to most learners. While educators do not always have the resources to support students' motivation individually, harnessing the social aspects of classroom learning is a manageable, effective strategy to elevate students' motivation. Learning environments that demonstrate empathy, inclusiveness, and psychological safety have shown positive results in students' behavior, self-esteem, motivation, and academic success [ 52, 53, 54, 55, 56]. Social motivation has been shown to enhance the encoding of new information (even if the content is not social) [ 57]. Learning-for-teaching and peer tutoring (one student teaching another student) effectively encode information into memory. Beyond memory improvement, peer tutoring has many further benefits to both the tutor and the learner in academic achievements [ 58, 59], motivation, and ownership over the learning process and results in a deep conceptual understanding of the material [ 60].

The teacher's demeanor is another controllable factor with a high potential to affect students' motivation. For example, the literature points to teachers' immediacy (creating physical and psychological closeness with students) as an effective way to enhance students' engagement, learning motivation, and performance (including memory retention) [ 61, 62, 63, 64, 65]. Immediacy can be demonstrated through verbal and non-verbal gestures that communicate interest and personal connection (relating to personal stories, using animated voice and body language, creating eye contact, and using humor).

The research also indicates that, when students perceive the content as being personally relevant, they are more motivated to study [ 66]. Therefore, educators can actively make the learning content more relevant by using stories and real-life examples, making explicit connections and demonstrations of how the content may be relevant/applicable to the students, and giving students opportunities to reflect and share their connections to the learning material.

In summary, physiological and psychological approaches point to primary motivational drivers that direct engagement and investment in the learning process. Not surprisingly, these drivers that are anchored around social and intellectual needs align with the conditions supporting neuroplasticity discussed in the first part of this review.

1.3. Intrinsic and Extrinsic Processing in Learning and Meaning-Making

As the environment provides more information than the brain can handle, survival depends on saliency detection and attention management to direct perception and behavior. The brain constantly selects and attends to relevant input while suppressing irrelevant or distracting information [ 67]. Information that is valuable or urgent for survival and prosperity receives attention. Attention capacities (e.g., alerting, orienting, and controlling attention) are managed by several brain systems that interact and coordinate [ 68, 69, 70]. Top-down, cognitive-driven attention that fosters a goal-directed thinking process is associated with the dorsal attention network (consisting of the intraparietal sulcus and the frontal eye fields) [ 71]. This mechanism enables students to read a paragraph, listen to a lecture, think about the teacher's question, or write an essay. A second attention system is bottom-up and stimulus-driven, and it orients attention to unexpected and behaviorally relevant stimuli. This ventral attention network consists of the right temporoparietal junction and the ventral frontal cortex [ 71]. This attention-grabbing mechanism enables the individual to respond quickly to urgent environmental demands, for example, moving away to prevent a struck-by-object accident. Flexible attention control depends on dynamic interactions and switching between the two systems and involves the central executive network (CEN) [ 68, 70].

The insula and anterior cingulate cortex comprise the core structures of the saliency network [ 72], another major player in attention altering to emotionally salient stimuli through the interaction of the sensory and cognitive influences that control attention [ 72, 73, 74].

In addition to outward-focused attention, the human brain is also invested in inward-focused processing. Functional brain imaging studies of the human brain show a robust functional anticorrelation between two large-scale systems, one highly extrinsic and the other deeply intrinsic [ 75, 76, 77]. The central executive network (CEN) is an externally driven system and is paramount for attention control, working memory, flexible thinking, and goal-directed behavior. The core components of the CEN are the dorsolateral PFC and the lateral posterior parietal cortex (hence, the frontoparietal network) [ 72, 78]. When the human brain is not occupied with external tasks, the default mode network (DMN) is activated. This internally driven cognitive network includes the posterior cingulate cortex (PCC) and the medial prefrontal cortex (MPFC) as core components. The DMN is thought to facilitate reminiscing, contemplating, autobiographical memory, self-reflecting, and social cognition [ 79]. Conversely, the DMN is immediately suppressed when the brain is engaged in externally driven tasks and stimulation.

Resting-state brain imaging studies revealed that the activity in the DMN during resting awake states indicates the quality of subsequent neural and behavioral responses to environmental stimuli [ 72, 80]. Moreover, a high connectivity between "intrinsic" (DMN) and "extrinsic" (CEN) brain networks, and specifically emotional saliency, attention (extrinsic), and reflection (intrinsic) networks is associated with better cognitive performance, meaning-making, and broad perspective thinking [ 75, 76, 81]. These networks function antagonistically but are highly connected and balance each other. Furthermore, the anticorrelation between their function is associated with better task performance and positive mental health [ 79, 82]. Recent studies also suggest that a causal hierarchical architecture orchestrates this anticorrelation between externally and internally driven brain activities. More specifically, that regions of the saliency network and the dorsal attention network impose inhibition on the DMN. Conversely, the DMN exhibits an excitatory influence on the saliency and attention system [ 79].

1.3.1. The Neuroscience of Extrinsic and Intrinsic Processing as Educational Content

Teaching students about the dynamics of the default mode and executive control network can help them understand how their brain processes information, the importance of each process (e.g., extrinsic and intrinsic), and their integration for meaningful learning. This knowledge can be applied as students explore and experiment with ways to enhance their learning and memory by intentionally engaging both intrinsic and extrinsic processing and integrating the two.

1.3.2. Using the Neuroscience of Extrinsic and Intrinsic Processing to Guide Learning Design

Traditionally, instructional education is based on learning objectives that are externally dictated and is focused on outward attention (stimulus-driven lectures and assignments). Mind-wandering has become the enemy of classroom teachers, as it indicates students' lack of attention and poor learning.

Nevertheless, neuroscience research indicates that meaning-making and cognitive performance benefit from the interplay between extrinsic and intrinsic oriented attention and processing [ 56, 75].

Learning instructions should consider the different attention mechanisms, evoking adequate arousal levels and leading to goal-directed thinking. Furthermore, students will benefit from an educational design that stimulates the natural interplay between "intrinsic" (DMN) and "extrinsic" (CEN) brain networks by incorporating external stimulation (e.g., presenting content), allocating time and space for intrinsic reflection (e.g., guided reflection and journaling), and integrating the two (e.g., guided class discussion and insights sharing) [ 83, 84].

2. Discussion

2.1. Teaching Students about Their Developing Brains

As far as we know, humankind is the only species with access to the underlying mechanisms of its perception, learning, and inner workings. In addition, the human brain is endowed with a lengthy developmental period of approximately 25 years [ 85, 86]. Therefore, schooling years are the prime time for neuroplasticity, and students can learn about their brains while they are highly malleable and can utilize this to amplify their learning and growth.

While students were traditionally required to choose whether to focus on the humanities or science fields, an integrative view is becoming increasingly common in academic institutes. Multidisciplinary studies have been shown to promote students' positive learning and professional outcomes [ 87]. Teaching neuroscience from a dual perspective, both scientific/objective and humanistic/subjective, is a novel but natural bridge between the humanities and science fields.

Studying neuroscience with explicit connections to the lived experience of brain development and its behavioral manifestation can be academically and personally transformative for students.

Shedding a scientific light on students' experiences as they unfold can support significant developmental processes during those years, such as improvements in executive functions, emotional regulation skills, meta-cognition, and social cognition [ 88].

Among the topics and burning issues of teens and young adults that neuroscience can offer insights into are selective and leaky attention [ 89], the reward system and addiction [ 90], the PFC-limbic developmental mismatch during adolescence [ 91], neurodiversity and inclusion, emotion regulation, and mental health [ 92].

Moreover, adding a personal layer to neuroscience studies fits the notion that personal relatedness and relevance are essential for learning motivation. Teaching neuroscience from a dual (scientific and personal) perspective and connecting neuroscience knowledge to a deeper understanding of the self and others can elevate engagement and nurture students' passion for science and their ability to integrate and transfer scientific knowledge across contexts. In addition, similar to the effect of physical education, educational neuroscience can promote the awareness of brain health and encourage students to be intentional about their education and developmental trajectory.

2.2. Teaching the Way(s) the Human Brain Learns Best

Teaching students in brain-friendly ways means implementing principles that align with how the human brain encodes, consolidates, and retrieves information. Educational neuroscience points to the importance of a holistic and integrated view of cognitive, emotional, and social aspects to support learning and development [ 52, 75, 93, 94]. Maintaining physical health, cognitive challenge, and emotional safety are essential factors in creating an enriched environment that supports neuroplasticity and learning.

Assessing the learning progress rather than the end product can encourage students to move away from rote memorization to more meaningful learning that carries on beyond the final exam.

Meaningful learning can be promoted by learning designs that encourage students to take experimental and explorative approaches, take risks, and make mistakes without detrimental consequences to their grades.

Furthermore, assessments throughout the learning process and not only at the end of it, using multiple sample points and low-risk tasks, can provide information on the student's learning curve and allow for personalized and timely feedback that students can apply to improve their learning on the go.

These methods not only promote psychological safety but also align with the evidence-based practices of building long-term and accessible knowledge by spreading out the learning concept across time (spacing), practicing information retrieval from memory (recall), and integrating and transferring knowledge (the application of knowledge in different contexts) [ 95].

3. Conclusions

Brain knowledge is brainpower; teaching students about their developing brain can support their academic and personal development by deepening their understanding of science and humanities, their mental capacity, and their self-identity. Educational neuroscience is a promising field in teaching students about their brains and teaching them in brain-friendly ways to support them in becoming lifelong learners.

Funding

This research received no external funding.

Institutional Review Board Statement

The study did not require ethical approval.

Informed Consent Statement

Data Availability Statement

The study did not report any data.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The zone of proximal development and neuroplasticity. An integrative approach between Vygotsky's educational model and the neuroscience of learning. When learning and performance demands exceed the available support and resources, students are likely to be overwhelmed and resort to survival mode (stress zone). When learning and performance demands are significantly lower than the available support and resources, students are likely to be under stimulated and resort to static mode (comfort zone). When learning and performance demands match the available support and resources, students are likely to be appropriately challenged and work within their zone of proximal development, which promotes neuroplasticity and growth (stretch zone).

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Growing Brains, Nurturing Minds-Neuroscience as an Educational Tool to Support Students' Development as Life-Long Learners

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Compared to other primates, humans are late bloomers, with exceptionally long childhood and adolescence. The extensive developmental period of humans is thought to facilitate the learning processes required for the growth and maturation of the complex human brain.

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4 Trends that will Shape the Future of Higher Education

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4 Trends that will Shape the Future of Higher Education

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"This shows that our brains do not learn by listening, and the little information we learn that way is easily forgotten. Real learning relies on principles such as spaced learning, emotional learning, and the application of knowledge."

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Education

Higher education needs to address the problems it faces by moving towards active learning, and teaching skills that will endure in a changing world. Image: Vasily Koloda for Unsplash

Senior Director, Strategic Communications and Thought Leadership, Minerva Project

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4 trends that will shape the future of higher education

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Higher education needs to reform by moving towards active learning, teaching skills that will endure in a changing world, and adopting formative assessment. Here are 4 higher education trends to watch out.