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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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Higher education is beginning to accept that traditional methods of teaching are ineffective - as demonstrated by the Ebbinghaus forgetting curve

Many people gain admission to higher education based on standardized tests that skew to a certain socio-economic class Image: Fairtest, College Board

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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.

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Rich students get better SAT scores—here’s why

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Researchers have repeatedly found that wealthy students enjoy significant advantages throughout the college application process, and that income greatly impacts a student’s performance on standardized tests.

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1 minute read

The Wiley fellows, a group of distinguished instructors from Wiley's partner institution network, recently looked at current trends to predict how the experiences, expectations, and needs of students and faculty may change, and how institutions and programs might evolve as a result. Click on the infographic below to learn how higher education might look by 2025.

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2025: A Look Into the Future of Higher Education

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See how one group of university faculty members predicts the experiences, expectations, and needs of students and faculty may change, and how that will impact the future of higher education for institutions and programs in this infographic.

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Interactive Brain | How the brain works & the impact of injury

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Take an interactive journey to see how the brain works and what impact an injury can have

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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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After watching this, your brain will not be the same | Lara Boyd | TEDxVancouver

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Jay McTighe has a varied career in education. He served as director of the Maryland Assessment Consortium, a collaboration of school districts working to develop and share formative performance assessments and helped lead standards-based reforms at the Maryland State Department of Education. Prior to that, he helped lead Maryland's standards-based reforms, including the development of performance-based statewide assessments.

Well known for his work with thinking skills, McTighe has coordinated statewide efforts to develop instructional strategies, curriculum models, and assessment procedures for improving the quality of student thinking. He has extensive experience as a classroom teacher, resource specialist, program coordinator, and in professional development, as a regular speaker at national, state, and district conferences and workshops.

McTighe is an accomplished author, having coauthored more than a dozen books, including the award-winning and best-selling Understanding by Design® series with Grant Wiggins. He has written more than 50 articles and book chapters and has been published in leading journals, including Educational Leadership (ASCD) and Education Week.
UNDERSTANDING BY DESIGN® and UbD® are registered trademarks of Backward Design, LLC used under license.

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Upgrade Your Teaching: Understanding by Design Meets Neuroscience

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How can educators leverage neuroscience research about how the human brain learns? How can we use this information to improve curriculum, instruction, and assessment so our students achieve deep learn

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Neuropedagogy as a Scientific Discipline: Interdisciplinary Description of the Theoretical Basis for the Development of a Research Field

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Neuropedagogy as a Scientific Discipline: Interdisciplinary Description of the Theoretical Basis for the Development of a Research Field

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Why don’t we cover the desert with solar panels? - Dan Kwartler

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One problem with standardized tests: We don't fully understand what they measure. On the face of it, they are designed to provide an objective appraisal of knowledge, or perhaps even of inherent intelligence.

But a recent study by Brian Galla, a psychology professor at the University of Pittsburgh, with Angela Duckworth and colleagues concluded that high school grades are actually more predictive of college graduation than standardized tests like the SAT or ACT.

That's because standardized tests have a major blind spot, the researchers asserted: The exams fail to capture the "soft skills" that reflect a student's ability to develop good study habits, take academic risks, and persist through challenges, for example. High school grades, on the other hand, appear to do a better job mapping the area where resilience and knowledge meet. Arguably, that's the place where potential is translated into real achievement.

"The more I understand what testing is, actually, the more confused I am," said Duckworth, a psychologist and expert on measuring human potential, when we interviewed her in 2020. "What does the score mean? Is it how smart somebody is, or is it something else? How much of it is their recent coaching? How much of it is genuine skill and knowledge?"

Yet standardized tests are still a mainstay of U.S. education. They play a critical role in deciding whether students graduate, what college or university they'll attend, and, in many ways, what career paths will be open to them. Despite the fact that they take a few hours to complete-a tiny fraction of the time students spend demonstrating their learning-the tests are a notoriously high-stakes way to determine academic merit.

By several measures, high-stakes tests are an inequitable gauge of aptitude and achievement. A 2016 analysis, for example, found that the tests were better indicators of prosperity than ability: "Scores from the SAT and ACT tests are good proxies for the amount of wealth students are born into," the researchers concluded. Even students who manage to do well on the tests often pay a steep price emotionally and psychologically. "Students in countries that did the best on the PISA [Programme for International Student Assessment]," for example, "...often have lower well-being, as measured by students' satisfaction with life and school," wrote Yurou Wang, a professor of educational psychology at the University of Alabama, and Trina Emler, a researcher at the University of Kansas.

We've almost certainly given too much weight to high-stakes tests, in other words, and increasingly the pressure of the tests is showing up as a serious health issue for students.

Biological Flares

As high-stakes tests loom, cortisol levels, a chemical marker for stress, rise by an average of 15 percent, a physiological response linked to an 80-point drop in SAT scores, according to 2018 research. For students who were already experiencing hardships outside of school-poverty, neighborhood violence, or family instability, for instance-cortisol spiked by as much as 35 percent, a level that is likely to derail cognitive processes and distort test scores beyond recognition. Are high-stakes tests sometimes measuring the impact of stressors like depression, family divorces, or the tests themselves, rather than knowledge?

The researchers also found that in a small group of students, cortisol levels dropped steeply during test-taking season, which they speculated had more to do with "shutting down in the face of the test" than handling the stress more effectively-in effect, triggering an emergency shut-off switch.

"Large cortisol responses-either positive or negative-were associated with worse test performance, perhaps introducing a 'stress bias' and making tests a less reliable indicator of student learning," the researchers concluded. This is a real problem, they warned, not only because elevated cortisol levels "make concentration difficult," but also because "prolonged stress exposure" burns kids out and increases the likelihood of disengagement and academic failure.

Sleepless Nights and Crises of Identity

In a 2021 study, Nancy Hamilton, a University of Kansas psychology professor, detailed the damaging effects of high-stakes tests on young adults.

Starting a week before consequential exams, college undergraduates recorded their study habits, sleep schedules, and mood swings in daily diary entries. Hamilton's findings were troubling: The anxiety caused by imminent, high-stakes tests leaked into daily life and were "correlated with poor health behaviors, including dysregulated sleep patterns and poor sleep quality," leading to a "vicious cycle" of cramming and poor sleep.

In an interview with Edutopia, Hamilton explained that instead of thinking about the academic material to be studied, many students became preoccupied by the life-changing consequences of the exams. Trying to fall asleep at night, they fretted about whether they'd get into a good college, worried about landing a job that paid well, and feared they'd disappoint their parents.

Without breaks, high-stakes tests can cause a host of cascading problems, Hamilton continued, including increased anxiety levels, overconsumption of caffeine, smoking, an unhealthy diet, lack of exercise, and poor sleep quality.

Test results are often tinged with a kind of existential dread. In a 2011 study, Laura-Lee Kearns, a professor of education at St. Francis Xavier University, discovered that high school students who failed the state standardized literacy test "experienced shock at test failure," asserting that they "felt degraded, humiliated, stressed, and shamed by the test results." Many of the students were successful in school and thought of themselves as academically advanced, so the disconnect triggered an identity crisis that made them feel as though "they did not belong in courses they previously enjoyed, and even caused some of them to question their school class placement."

"I enjoyed English, but my self-esteem really went down after the test," a student reported, echoing a sentiment felt by many. "I really had to think over whether I was good at it or not."

Early Psychological Impact

High-stakes testing commonly begins in third grade, as young students get their first taste of fill-in-the-bubble scantrons. And while the tests are commonly used as diagnostic tools (presumably to help tailor a student's academic support) and to evaluate the performance of teachers and schools, they can come with a bevy of unintended consequences.

"Teachers and parents report that high-stakes tests lead to higher levels of anxiety and lower levels of confidence on the part of elementary students," researchers explained in a 2005 study. Some young students experience "anxiety, panic, irritability, frustration, boredom, crying, headaches, and loss of sleep" while taking high-stakes tests, they reported, before concluding that "high-stakes testing causes damage to children's self-esteem, overall morale, and love of learning."

When asked to draw pictures portraying their test-taking experience, the students in the study overwhelmingly cast their ordeal in a negative light-a depiction of a "nervous" student predominated. "Students were nervous about not having enough time to finish, not being able to figure out the answers, and not passing the test," the researchers explained. In nearly every drawing, the children drew themselves with "unhappy and angry facial expressions." Smiles were nearly nonexistent, and when they did occur, it was to show relief that the test was over, or for unrelated reasons, such as being able to chew gum during the test or being excited about an ice cream celebration after the test.

Manufactured Power

Tests like the SAT and ACT aren't inherently harmful, and students should learn how to manage reasonably stressful academic situations. In fact, banning them completely might be counterproductive, denying many students a critical avenue to demonstrate their academic skills. But to make them a condition of matriculation, and to factor them so prominently in internal ranking and admissions processes, inevitably excludes millions of promising students. In a 2014 study, for example, researchers analyzed 33 colleges that adopted test-optional policies and found clear benefits.

"The numbers are quite large of potential students with strong high school GPAs who have proved themselves to everyone except the testing agencies," asserted the researchers. High-stakes tests too often function as arbitrary gatekeepers, pushing away students who might otherwise excel in college.

If recent events in California are any indication, high-stakes tests may be in decline. Last year, the University of California dropped SAT and ACT scores from its admissions process, delivering a "resounding blow to the power of two standardized tests that have long shaped American higher education," the Washington Post reported. Meanwhile, hundreds of colleges and universities that dropped testing for pandemic-related reasons are reconsidering their value -including all eight Ivy League schools.

"This proves that test-optional is the new normal in college admissions," said Bob Schaeffer, FairTest's Public Education director, in the New York Times. "Highly selective schools have shown that they can do fair and accurate admissions without test scores."

In the end, it's not the tests-it's the almost fetishistic power we give to them. We can preserve the insights that the tests generate while returning sanity and proportionality to a broken system. Quite simply, if we deemphasize high-stakes tests, our students will, too.

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The Psychological Toll of High-Stakes Testing

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High-stakes tests often create enormous anxiety and return muddy data. Turning down the pressure may alleviate the problem.

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Examples

In addition to the three core styles, over 71 separate learning-style instruments and theories have been documented in education literature (Coffield et al 2004). 30 popular ones include (Pashler 2008):

· convergers vs. divergers

· verbalizers vs. imagers

· holists vs. serialists

· deep vs. surface learning

· activists vs. reflectors

· pragmatists vs. theorists

· adaptors vs. innovators

· assimilators vs. explorers

· field dependent vs. field independent

· globalists vs. analysts

· assimilators vs. accommodators

· imaginative vs. analytic learners

· non-committers vs. plungers

· common-sense vs. dynamic learners

· concrete vs. abstract learners

· random vs. sequential learners

· initiators vs. reasoners

· intuitionists vs. analysts

· extroverts vs. introverts

· sensing vs. intuition

· thinking vs. feeling

· judging vs. perceiving

· left brainers vs. right brainers

· meaning-directed vs. undirected

· theorists vs. humanitarians

· activists vs. theorists

· pragmatists vs. reflectors

· organizers vs. innovators

· lefts/analytics/inductives/successive processors vs. rights/globals/deductives/simultaneous processors

· executive, hierarchic, conservative vs. legislative, anarchic, liberal

A variety of sites in the consumer industry, including VARK, Learning Styles Online, and Education Planner encourage students and learners to 'find their learning style,' exhibiting a) how wide and popular the notion has become in culture, and b) the ways that online inventory and profiling quiz culture can warp educational research. Instructors might prepare for their students to arrive in class with certain assumptions about their most efficient modes of learning.

Recommendations

Many instructors still gravitate towards the learning-styles framework because of its seeming intuitiveness and resonance with teaching experiences. A broader approach that invites students to reflect on their learning, rather than narrow their style down, has been shown to improve learning outcomes (Ambrose et. al, 2010).

Studying vs. Learning - Instructors can help students understand the difference between studying styles and learning processes. Students will develop their own preferences for reviewing content, but these practices differ from deeper cognitive processes like "chunking," building on prior knowledge, making conceptual connections, and transferring knowledge. Ambrose, et. al (2010) and the National Research Council (2000) offer excellent overviews of these deeper processes, and explain why multiple modes of instruction assist all students.

Students benefit from different kinds of instruction - Because learning requires complex, often uneven developmental steps like building on prior knowledge, forming conceptual structures slowly, and varieties of repetition, students benefit when instruction provides various ways to enter into learning. Alternating modes can serve different students' aptitude, level of self-awareness as a learner, and cultural background. Instructors should imagine students to be neither uniform, nor categorized, in their learning, but instead experiencing similar development through singular personalities and experiences. As such, instructors can incorporate active learning, group work, and inclusive teaching strategies to invite students to engage their full faculties and experience peer learning. Multiple modalities can assist all students regardless of proposed learning style: research shows, for instance, that students learn more deeply from words and visuals than from words alone. Multimedia presentation encourages active cognitive processing, promoting meaningful learning (Mayer 2003).

Students benefit from thinking about how they learn - Research shows that students benefit when given opportunities to reflect on assignments, exams, and activities, and that learning outcomes improve when instructors help students think about how they drew connections, digested content, or arrived at conclusions (Kaplan, et. al, 2013). This process of metacognition helps students think about their thinking, and helps students identify ways to improve their learning and avoid weak habits of thought or study.

Effective instructional methods can vary across disciplines and course content - Proposed learning styles do not always fit disciplinary norms; for example, writing courses benefit from a significant verbal component, geometry courses from a visual component, and lab classes from an experiential component. Instructors can be aware of predominant presentation styles in their discipline, and consider discipline-specific resources to widen their modes of instruction.

References

Ambrose, S., Bridges, M., Lovett, M., DiPietro, M., & Norman, M (2010). How Learning Works: 7 Research - Based Principles for Smart Teaching. San Francisco: Jossey-Bass.

Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. London, UK: Learning and Skills Research Centre.

Kaplan, M., Silver, N., Lavaque-Manty, D., and Meizlish, D., eds. (2013). Using Reflection and Metacognition to Improve Student Learning. Sterling, VA: Stylus.

Kirschner, PA. (2017). Stop propagating the learning styles myth. Computers & Education, 106: 166-171.

Mayer, RE. (2003). The promise of multimedia learning: using the same instructional design methods across different media. Learning and Instruction, 13(2): 125-139.

National Research Council. (2000). How People Learn: Brain, Mind, Experience, and School: Expanded Edition. Washington, DC: The National Academies Press.

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning Styles: Concepts and Evidence. Psychological Science in the Public Interest, 9(3), 105-119.

Simmonds, A. (2014). How neuroscience is affecting education: Report of teacher and parent surveys. Wellcome Trust.

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Examples In addition to the three core styles, over 71 separate learning-style instruments and theories have been documented in education literature (Coffield et al 2004). 30 popular ones include (Pashler 2008): · convergers vs. divergers · verbalizers vs. imagers · holists vs. serialists

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What College Teachers Should Know About Memory_ A Perspective From Cognitive Psychology

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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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Apostolos Koutropoulos
University of Massachusetts
Boston, MA 02125 USA
a.koutropoulos@umb.edu

This year marks ten years from the first introduction to the term "Digital Natives," coined originally by Marc Prensky in two seminal articles that established the traits of the digital native (2001a, 2001b). Prensky is by no means the first person to try and identity a new, distinct, generation of humans, and digital natives isn't the only term proposed to describe them. Other common terms come from Oblinger & Oblinger (2005) and Tapscott (1997) who use the term net-generation, and Strauss & Howe (2000) use the term Millennials to describe this same generation of students. The dates do vary from author to author, but not by much; and the characteristics attributed to this new generation blend easily from author to author because a lot of the characteristics are predicated on access and utilization of technology and the Internet. This naming fetish didn't stop at these three terms, and these terms are used interchangeably in every day discussions, but for the purposes of this article, this article will use the term "digital native."

The emergence of the classification of this new generation lead to many articles, blog posts, columns, books and videos on educating this generation and taping into these learners' new learning styles (Dede, 2005). Popular viral videos were produced showing statistics of new technology usage (Socialnomics, 2009) and emotional pleas from self-professed young digital natives that would like to see change in the institutions that educate them and cater to their unique digital needs and learning styles (VALATV, 2010; Nesbitt, 2007) What's amazing about this term, and all that it implies, is the amazing speed with which it took hold in circles broader than the educational circle, and how it became part of our "common sense" without having been tested much in every day practice. The digital native became a rallying cry for change in education, (expensive) technological infusion at all levels of education, and broad-changes in institutions that are providing learning opportunities and environments to these digital natives. This article aims to critically examine the underlying digital native theory, review recent studies inquiring about the existence of digital natives, and to examine the effects that this "movement" has had on the learning professions.

When examining the key characteristics of digital natives I start by examining the works of Prensky (2001a, 2001b, 2003, 2006a, 2006b, 2010) for two reasons. He is the author most often quoted or referred to when discussing the characteristics and preferences of this generation in both peer reviewed and non-peer reviewed articles; especially in the "early literature" on digital natives between 2001 and 2006. He is also the person who coined the term "digital native" in his early writings. Other authors, such as Dede (2005), Oblinger (2005), Oblinger & Oblinger (2005), Howe & Strauss (2000), Tapscott (1999) and Frand (2000) also get referenced, although not with the same frequency as Prensky's writings. Thus it makes sense to examine Presky's arguments and positions first.

Prensky's Digital Native Canon

In his first article on digital natives (2001a) Prensky writes about a singularity, an event that fundamentally changed things, our students in fact, so that the current educational system is ill prepared for this new generation of learner. Prensky supports his argument by providing some facts and figures, such as students spending fewer than 5,000 hours of their lives reading (assuming that reading refers to books only), but over 10,000 playing video games, 20,000 watching television, and he goes on to give us more similar statistics about Instant messages (IMs) sent, talking on (digital) cellphones and sending email. It is clear that in Prensky's writings, as well as other digital native author's writings, that they expect that these statistics hold true across the board, regardless of your socioeconomic background and your country of origin. What's clear is that the context isn't really considered. Who is spending all this time playing video games? Prensky owns a video game company so it may be that what he sees every day is what he thinks of the norm, but that doesn't mean that this norm is universal reality.

Other overgeneralizations put forth by authors like Prensky, is that the digital natives prefer images over text, they prefer games over "serious work," they function best when networked, digital natives can't pay attention (or they choose not to!), and finally digital natives have skills, with digital technologies, that they've perfected. There are a number of issues with these overgeneralizations. First, and foremost, what is the context? What is the Socioeconomic Status (SES) of these digital natives that have access to the technology that they've become skilled masters of? Do those general skills transfer over to the academic side? Could I seamlessly take my skill of posting facebook updates and apply this to an academic context without the help of a more experienced "digital immigrant"? Can Prensky's digital immigrants just pay attention even when they are bored? Of course not! Digital immigrants doodled, dozed off and day dreamt when they were bored, digital natives do the same. Being able to focus on a task has nothing to do with exposure to technology. Finally, I would say, that most people prefer to do fun things rather than something that they perceive of as work.

These figures and overgeneralizations have oft been repeated by followers of the digital native message, without much self-reflection or critique. They are taken as-is, as common sense, and as universal. Early on, when some suggested that the context does matter and that these are assumptions aren't universal (VanSlyke, 2003), Presky's (2003) rebutal, which included a lot of technological buzz words and no hint of pedagogy, said that these online services are a few of the things that kids are using online these days and he expects that his observations hold true in the US, Japan, Korean and much of the rest of the world. Prensky doesn't provide facts or empirical evidence, just suppositions. When Prensky writes about his preferred method of teaching (2001a) he writes that he prefers to invent video games, but he never considers that this may not be the most appropriate method of instruction, and that it might not be the learner's preferred method for instruction. Thus in the same article he talks about the needs of the learner, while at the same time ignoring the needs of learners by imposing his own preferred method of teaching.

Prensky's technological determinism culminates in a biological determinism in part 2 of his introduction to the concept of the digital native (2001b). Prensky argues that the brain's neuroplasticity makes it so that the brain adapts to the environment that it is in, so in a technology-infused environment the brain will adapt to better use the tools that are available in that environment. While this may be true, there are two things that Prensky does not take into account. The first is that as human beings our brain is continuously rewiring itself throughout our lives. We don't fossilize at a specific state of our lives, but we learn to use the tools that are available to us, thus digital natives should also exploit that physical ability to learn to function in environments that don't necessarily have the tools that they are used to. The second thing that Prensky never questions, in either article, is the need to impose radical change on our educational system. This, taken together with the, unknown at the time, numbers of technology use within the digital native population, means that we weren't really talking about pedagogy, and what's really good for the learners, but rather, perhaps, change for change's sake, or the technological equivalent of "throwing money at the educational problem."

Addenda and Extensions to the Canon

Other digital native evangelist authors have their own two cents to add into the mix of characteristics that defines this group of supposedly tech-savvy students. Oblinger (2005) for instance portrays a vision of technological utopia, something that supposedly exists today, where students are proactively using their iPods to learn, snap photos everywhere they go and use these tools for impromptu study meet-ups. Students don't wait for the professor to answer their question but they Google it for themselves and find the answer; however this isn't always the truth. The fact that one can mechanically go through the motions of searching for someone on Google doesn't mean that they possess the critical literacy and information literacy required to determine which results are quality search results. Even if they knew which results were quality results it does not imply that these students can then process what those results mean and to learn the answer to a question that they had; this is something that Oblinger glosses over.

One of Frand's (2000) contributions to what makes a digital native a digital native is what he terms "Nintendo over logic," which claims that this generation doesn't read manuals and prefers a trial and error approach, as one might find in a video game. Prensky (2001a) and others also hit upon this note as an innovation of digital natives, and an "accent" (or hinderance) that digital immigrants have because they rely on manuals. This notion of trial and error über alles as a innovation of digital natives, and the non-reliance on manuals is false on two grounds. First, experiential learning, another name for learning by trial and error, goes back at least to the early 1900s with the work of Piaget. Presky's later proposals (2006a, 2006b; 2010) for using peer groups, allowing students to pursue their passions, and essentially going from a s age on the stage to a guide on the side aren't new, but they go back to Piaget (Singer & Revenson, 1996), Vygotsky (1978) and even Socrates (Karasmanis, 2002), just to name a few. If Piaget, Vygotsky and Socrates thought of these notions, this means that these traits aren't inherent to a population who grew up in a digital age, but rather these are traits inherent in humans as a whole, and everything else is just a tool that we can utilize.

Another trait that is ascribed to digital natives is that they are multitaskers, moreover they are efficient at it, and it is technology that encourages this multitasking. Dede (2005) gives us an example of his daughter doing her homework while at the same time listening to music, emailing and IMing friends and surfing the net. Dede isn't the only one who provides us with anecdotes like this one, and as a matter of fact there are many anecdotes like this in both peer-reviewed and non-peer reviewed publications, it's part of what passes as "common sense." The problem is that this type of multitasking wasn't taken to task. Are these tasks performed at the same time, or are they done serially, in smaller distinct segments? Has the efficiency of multitasking been proven? And how much brainpower are we giving to each individual task? Is the music something we pay attention to or is it the equivalent of white noise? Does the multitasker pay the same attention to each individual IM window or does he let some of them acquiesce?

Finally, tying in with multitasking, and "Nintendo over Logic", we have a depiction of how digital natives learn. According to Tapscott (1999) digital natives are non-sequential with their use of information, going back and forth between programs and sources and their learning style is an outgrowth of these ingrained habits of seeking and retrieving information from the Internet. This marks a striking contrast to previous generation of students, who tend to acquire info more passively from authority figures (Tapscott as quoted in Barnes, Marateo & Ferris, 2007). Perhaps one of the bigger claims made is that this generation (i.e. digital natives) exists across the world and across socio-economic conditions, not just in advanced economies (Tapscott as cited in Jones & Healing, 2010).

Just as with the number of email messages sent and read, the number of hours spent on watching TV and playing video games that Prensky (2001a) gives us, the importance is not in the metrics themselves, but rather the details of who provided this information, in what context, and what these numbers mean in the broader context. Knowing that one can multitask isn't new information, we've always been able to walk and chew gum at the same time, to borrow from the old saying, but how much attention are we paying to each task and why. The devil is in the details and unfortunately the early literature on digital natives that built upon the work of Presky, Oblinger, Tapscott, Dede and Frand lacked that fine attention to detail; they seemed to rework the same old assumptions, and fit their data within the Weltsaschauung of the digital native proponents.

Straight from the Digital Natives

Some information about digital natives comes from digital natives themselves, not just from members of others generations trying to describe digital natives. Two prime examples, from written sources, are Roberts (2005) and Windham (2005) who provide us with information about what it means to be a digital native from a technological and a social perspective. Roberts writes that he is " a member of the [digital natives] Generation. The Internet and related technologies have had a major influence on [his] generation's culture and development. Many, if not most, [digital native] students have never known a world without computers, the World Wide Web, highly interactive video games, and cellular phones."

Roberts continues by providing views of a supposed representative sample of digital native students and what technology means to them. Among the responses are "Reformatting my computer system and installing cutting-edge software that allows me to do what I want, when I want, without restrictions, viruses, and the rules of Bill Gates, " and "The ability to adapt and configure an already established program to [something that] benefits me daily." Roberts concludes with:

"The feedback from this select set of [digital native] students does contain some good news. It indicates that the [digital native's] general expectations regarding leading-edge technology have not fully impacted its expectations about the use of technology to support learning. This may signal a failure in the responsiveness of colleges and universities in terms of keeping pace with the rapidly changing technological landscape. However, it may also indicate that the opportunity to catch up with the [digital native] has not been lost."

There are several issues with what Roberts writes. An implicit assumption is made that his own lived experiences are the same across the lot of digital natives, something which isn't true. Up until recently access to video games, cellular phones and the Internet were costly enough to imply a certain level of affluence; as such these would not be the lived experiences of most members of this generation. Related to this is this assumed technological know how required to format, reinstall and customize one's computer (to the extent described); again this isn't the case with all digital natives. Finally, the digital native is viewed from a position of superiority, giving non-natives a chance to catch up with them, and the digital native's low expectations regarding academic technology must be a result of the non-native's failure to adapt quickly enough.

From a social perspective on digital natives we look to Windham (2005) who writes about encounters with a professor that one, reading her article, would characterize as a Luddite. While it is true that this particular professor could incorporate some newer ways of communication (like email) this particular digital native comes off as highly disrespectful to people who don't share her social conventions, relegating these non-digital natives into the category of "relics." This particular professor represented for her "a world [she] could scarcely remember-a world before driving directions on MapQuest, book buying on Amazon.com, and making plans on Instant Messenger." While this may be true it is hardly a reason to assume that all of the world operates in this same fashion, and that it is the only way of being.

Windham writes that members of the digital native generation are consumed by the notion of achievement, they join clubs, participate in community service and are engaged. Furthermore this generation has not seen corruption of power or felt the fear of the Cold War. It is argued that it is this type of social change that has brought on the technological adeptness in digital natives. Digital natives keep their addresses and plans in their PDAs, they listen to their music on MP3 players, record one television program while viewing another, and browse library databases instead of going to the library. These are viewed as digital native innovations over previous generations; however it is previous generations that brought these innovations to this current generation, the digital natives, not the other way around.

Finally, digital natives are described as striving "to stay ahead of the technology curve in ways that often exhaust older generations," and to achieve this they "rarely pick up the instruction pack to learn programming or a technique. Instead, spurred by our youthful exploration of the Internet, we tend to learn things ourselves, to experiment with new technology until we get it right, and to build by touch rather than tutorial" (Windham, 2005). From a surface view, this does corresponds to what other digital native evangelists claim, like Frand's (2000) "Nintendo over logic," but something is amiss here. Digital Natives may indeed start without looking at a manual, but when what they are using is not intuitive, they either get the manual, as is exemplified by the great numbers of computer game walkthroughs online; they will give up, as we shall see digital natives aren't that great at adapting when compared to older students; or they stick to what they know, which means not experimenting and goes counter the claims of digital native evangelists.

Demographics Matter

So, what does the research on digital natives tell us? The picture is not as black and white as digital native evangelists, would have us think. There are many variables that go into creating the stereotypical digital native. Location is important, as well as socioeconomic status. How much one uses a certain tool or technology, and what they use it for, are also factors; and how well those skills and behaviors transfer over into educational domains is important. After all, one can't advocate for radical change in the education system based on skills and behaviors that don't carry over.

VanSlyke (2003) had originally questioned the global reach of the digital native, and Prensky, in a rebutal, disagreed with him stating that he expected children in much of the rest of the world to exhibit the behaviors of the digital native (2003). Research, however, has shown that the location does matter. In the US (Smith & Caruso, 2010) we see different levels of computer and web technology usage among the same demographic of digital natives in Australia (Kennedy, et al., 2010; Margaryan & Littlejohn, 2008) and than those in the UK (Stoerger, 2009). In South Africa, as well, we see that only 26% of the population might be described as having grown up digital (Brown & Czerniewicz, 2010).

We also see that the semantics of the words "native" and "immigrant" make a difference. As Brown & Czerniewicz (2010) inform us, in the South African context the words "native" and "immigrant" have the opposite meaning from what Prensky (2001a) describes within his North American frame of reference. In South Africa, due to its colonial and apartheid past, the immigrant, the colonizer, is seen as the forward thinker, while the native is seen as the one who is backward. Digital native, as a term, according to many, including Brown & Czerniwicz, is an "othering" concept. It sets up a binary black and white, and those who fall into one category, whatever the connotation and the qualities implied, cannot exhibit characteristics of the other category that they are not members of.

This is problematic because there is an implied power relation, and superiority, attached to those particular sets of skills and dispositions of the digital native. In turn, this digital divide has spurred a Moral Panic, calling for radical change in education where arguments are articulated in dramatic language, with no empirical evidence or theoretical foundations, based only on "common sense" and personal anecdotes (Bennett & Maton, 2010). Anyone who resists or questions these calls radical change is said to be out of touch, lazy, or just dismissed as not having legitimate concerns (Jones & Healing, 2010).

Another major factor in this discussion are socioeconomic factors. Most of these studies poll students who are in college. College students don't represent whole populations because they tend to be from a segment of the population that has the financial capacity to afford to be able to go to college (Bradley, et al., 2008 in Bennett & Maton, 2010). As Brown & Czerniewicz (2010) framed it: it's not about a generation but an elite. Brown & Czerniewicz found that information and communication technology (ICT) use was along socioeconomic lines. Twenty-two percent of the students lacked both experience and opportunity to use ICT, had less than 4 years of experience and had no ICT access off-campus.

As Broos & Roe (2006) indicate, socioeconomic factors, as well as other factors such as race, gender and educational background, do play a role in how and how much people use technology. Those with higher incomes do tend to use technology and the Internet more often than those in middle and lower income categories. One's anticipated job prospects, in the Broos & Roe study, were also strong indicators as to whether one would use ICT or not. Finally, another interesting point to consider are the demographics of digital natives. In a recent US study by the Pew Internet and American Life Project (Rainie, 2011), most digital natives are white (61%) and live in the suburbs (54%). It is hard to believe that an American suburban white person will have the same life experiences as other people who are not in that same demographic group.

Access to, Utilization, and Quality of Engagement

With all the focus on having a generation "bathed in bits" (Tapscott, 1999) access to technologies and utilization of these technologies in both quality and quantity should be something that matters when we measure technological engagement of these digital natives. Access and utilization should also be something that ought to be scrutinized and carefully analyzed. First, keeping an eye toward the USA, we see that contributional usage by members of the digital native generation on social sites is quite low. Only 36% of digital native students contribute to blogs, only 40% contribute to wikis, and only 42% contribute to video sites. Social games and social bookmarking sites are only used by 25% of these digital natives. Fewer that 20% of the students said that they used course lecture podcasts or videos (Smith & Caruso, 2010). Similar results were also found by the Corrin, Bennett & Lockyer in the Australian academic context (2010) and the Pew Internet Internet and American Life Project in the broader US context (Fox & Madden, 2006; Jones & Fox, 2009; Zickuhr, 2010; Rainie, 2011).

Across the pond in England, Jones & Healing (2010) report that over 80% of first year students reported a "slight confidence" and "basic skills" with presentation software and online library resources - sources that they were familiar with. For sources that they were not familiar with, like blogs and wikis, these students reported that they didn't not feel confident in using them; this mirror's Kvavik's findings (2005) in the US. The Jones & Healing study reports that only a minority of students felt like it was important to them to share and upload content.

In Australia a study found that only 15% of the digital natives were "power users" and 45% were rudimentary technology users (Kennedy, et al., 2010). In a related study, more than 70% of Australian first year students never kept a blog, more than 80% had never produced a podcast and have never contributed to a wiki (Kennedy, et al., 2007). Similar results were reported by Corrin, Bennett and Lockyer (2010) indicating that only 23% of students self-reported as advanced computer users, 66% never had a blog, 69% did not maintain a website, video editing or creation was rare, and they seldom (31%) or never (41%) listened to podcasts. While it is true that the technological advancements of the past few decades have created a veritable cornucopia of information for digital natives and non-digital natives alike, as Dresang (2005) points out, the digital natives are missing out on this rich environment because they have poorly developed information-seeking skills, in other words they consume from sources that they already know.

If one were to take the claims of digital native evangelists at face value, or read student commentary such as the ones provided by Roberts (2005) and Windham (2005), or view popular viral videos by so-called digital natives (VALATV, 2010; Nesbitt, 2007), one might think that all of them are power users; that they are indeed media producers, that they collaborate often, frequently and with great skill. However the collected statistics from a variety of studies paint a different picture; the fact is that the average "digital native" entering college is not technologically sophisticated; this digital native is not a power user. Even in countries where there is more access to a computer and the Internet, usage of these technologies tends to be read-only, checking facebook or looking things up on Wikipedia (Selwyn, 2010; Margaryan & Littlejohn, 2008); in other words passive interaction.

Personal Technology and the Leap to Educational Technology

Even when students use a certain technology for personal use, it neither means that they want to use that technology for use in educational ventures, nor does it mean that they know how to use it in an educational context. As Kennedy, et al. (2008) discovered, the transfer from an entertainment technology to a learning technology is neither automatic nor guaranteed. Kvavik's study (2005) informs us that even high levels of skill and use did not necessarily translate into a preference for increased use of technology in the classroom; in addition students preferred technology in moderate mounts as a supplement to their courses. There are two factors in play here; first are student attitudes toward instruction, and the role of technology in instruction; and second the nature of personal technology when mixed with instruction.

First one has to examine student motivations and perceptions as to what constitutes instruction. Nicholas (2008), through his surveys, found that the majority of students prefer lecture as the format of class instruction, in addition to collaborative group work; a similar result was seen in an Australian study (Margaryan & Littlejohn, 2008). Lohnes & Kinzer (2007) discovered that student's perceptions of education were fairly traditional: students expected to go to a classroom and viewed this space as a separate space. They also expected to be lead through the materials by a subject matter expert. Technology isn't necessarily ruled out, however it needs to tie into the subject of the class, in other words, is the application of this technology appropriate for the subject matter? In Kvavik's study (2005) he discovered that students did not feel that the use of information technology in the class increased the amount of time that they were engaged with course activities. Some students (JISC, 2007 in Bayne & Ross, 2007) actually go a step further and suspect that if all learning in mediated through technology, the value of learning will diminish.

The second factor has to do with the perception of the space: is it a private space or a public space? Students are reluctant to mix their private sphere, exemplified by the use of services like facebook and instant messaging, with the classroom sphere. Students were asked if they would like to use their favorite IM client to support classroom communication. This was rejected by students, saying that their "friends" on MSN are actually their friends, so adding many classmates to their MSN account, with whom they generally don't speak, is too laborious, and potentially an issue of privacy (Margaryan & Littlejohn, 2008). An interesting adjunct to this are the findings of Jones & Romanau (2009) whose study found that "collaboration and collaborative learning did not seem to be a strong feature of the students' experience at university and the kinds of social networking that was done was mainly informal and largely unrelated to formal learning."

Locus of Control

One final element to consider is student locus of control and the independence to experiment freely (and without consequence) with the technology. Kvavik (2005) found that in quantitative studies students say that they have the skills that they need, however qualitative data contradicts the collected quantitative data. Students only have very basic office suite skills, and they have difficulty moving beyond those basic activities; it would appear that these students don't recognize that their applications have enhanced functionalities that they can use. The same study, discovered that these digital natives are mystified by technology and some are afraid to putz around, to experiment, for fear that they will do something wrong and break the computer; Margaryan & Littlejohn (2008) found similar results. This casts further doubt on the "Nintendo over logic" assertions that digital native evangelist authors have put forth.

One possibility for this lack of confidence on the part of computer users may lie in how much time they've spent using technology. Studies have shown that those who have taken up a technology more recently, and who lack confidence, take up fewer opportunities (Linvingstone & Helsper, 2007). Older students (the assumption being non-digital native older students) have been shown to have more proficiency with technology (Eynon, 2010), indicating that greater time on task is beneficial. Control of access has also been shown to have an affect on computer literacy. Young people who use the Internet more at school are more likely to use the Internet for homework activities; however sporadic and monitored access at schools and libraries may provide sufficient access for basic information seeking, but is insufficient for the immersed kind of social engagements that we expect students to possess (Ito, et al., 2008: p.2 in Eynon, 2010).

Educators are perhaps falling into the same trap as parents are; that is that we have a tacit expectation that kids will have spontaneous engagement with schooled interests spurred by the availability of the computer as a tool (Kerwalla & Crook, 2002), this of course has proven to not be the case. If computer access and behaviors are controlled, either by supervision by an authority figure, or inconvenient placement of the computing device, the locus of control is external (Kerwalla & Crook, 2002), and this has an effect of inhibiting creative experimentation, and thus limiting the learner. These limitations lead to computer usage behaviors are counter to what "common sense" has us associate with digital natives.

Finally, from a social perspective, in contrast to the earlier writings about digital natives by digital native evangelists that portrayed digital natives as masters of their own destiny, there is some work that goes counter to this argument. Johnson (2006), a digital native herself teaching other digital natives, indicates that digital natives are complacent, actively seek authority figures and are unable to cast a critical gaze on their lives. This can be seen in digital native's own writing as exemplified in the previous section by Roberts (2005) and Windham (2005).

In the past ten years, a lot of articles have been written about how to reach out and how to teach these so-called digital natives. Many professionals, in many diverse fields, have written on the topic and they, like Smith & Caruso (2010) believe some version of the myth that these digital natives are cloud-savvy information consumers. Much of this thinking is seen as "common sense," however as Ng informs us "common-sense thinking is uncritical, episodic, and disjointed, but it is also powerful because it is taken for granted" (1997, in Selwyn, 2009). Looking at the research, however, we see that there is no one, monolithic, group that we can point to and say that those are digital natives. As a matter of fact, the individuals who would fit the stereotype of the digital native appear to be in the minority of the population.

The person who coined the term digital native, Mark Prensky, acknowledges this fact in his recent writings by saying that "by virtue of being born in the digital age, our students are digital natives by definition, but that doesn't mean that they were ever taught everything (or anything, in some cases) about computers or other technologies, or that all of them learned on their own" (p64, 2010). The problem is that he, and other digital native evangelists, are still clinging to this flawed concept of the digital native. At best it doesn't describe a population because digital technology was in our day-to-day life before the digital native came along; at worst it is an "othering" concept, a concept which creates an artificial dichotomy with a privileged and a non-privileged position. Depending on your own cultural context, the native may be the privileged or the non-privileged one.

In earlier writings about digital natives (Frand, 2000; Prensky 2001a; Prensky, 2001b; Dede, 2005; Oblinger, 2005; Oblinger & Oblinger, 2005) the native was clearly superior, however uncritically accepting such terminology not only creates a second class citizen of the non-natives, but it also places the majority of digital natives in a depriviledged position. These digital natives try to catch up to the stereotype; obstacles are added to the education of the digital native due to the moral panic (Bennett & Maton, 2010) created by the calls for radical change in education to help these digital natives with the tools that they supposedly use every day.

Student surveys showed that older students more likely to change their approach to learning, whereas the digital native students were the least likely to change their approach to learning (Garcia & Qin , 2007). As Ramaley & Zia (2005) point out, "by the time students enter high school, disengagement from course work and serious study is common." From a US context, in a post- No Child Left Behind USA, if our digital native learners aren't engaged, they have no incentive to work around the problem and find a solution. In contrast, older learners, I would posit, are more engaged and thus do work at changing their approach in order to find solutions.

Instead of having education professionals focus on the technology aspect of the debate and in certain digital native behaviors, which "common sense" has told us, are immutable, we ought to be focusing on proper pedagogy and exposing our students to information retrieval and critical information analysis skills that are in both the digital and the analog realms. We out to teach our students to actually change their approaches to learning when what they are trying out is not working for them, instead of assuming that they possess this "Nintendo over logic" which enables them to modify their learning plans when things aren't working out.

Finally, we need to move away from this fetish of insisting in naming this generation the Digital/Net/Google Generation because those terms don't describe them, and have the potential of keeping this group of students from realizing personal growth by assuming that they've already grown in areas that they so clearly have not. Learners don't know what they don't know (Christensen, 2006), but if they come to the table from a position of superiority, like they are better than the so-called digital immigrants (Roberts, 2005; Windham, 2005) they lose an opportunity to learn something that they don't know that they don't know, something that may be beneficial to them. Let's resist "common sense" because common sense isn't all that common.

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The Journal of Online Learning and Teaching

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UC 199 Part 2: Processes - Building Memory Power

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Part 2: Processes - Building Memory Power Animation; Dr. Michelle Miller

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RED, you should say "Blue."
As soon as the words appear on your screen, read the list as fast as you can. When you have finished, click on the "Finish" button. The time it took you to read all of the words will be shown. If you want to try the same set of words, click on the "reload" button of your browser. If you want to continue with the experiment, click on "Continue Experiment."

Go to the first test.

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RED, you should say "Blue."As soon as the words appear on your screen, read the list as fast as you can. When you have finished, click on the "Finish" button. The time it took you to read all of the words will be shown.

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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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Retrieval practice is a simple research-based teaching strategy that dramatically raises students' grades. When students retrieve and bring information to mind, this mental challenge produces durable long-term learning. Easy learning leads to easy forgetting. Stop cramming, reviewing, and re-teaching. Instead, simply ask students what they remember. No prep, no grading, just powerful teaching.

The science of learning exists. It's time to unleash it.

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Retrieval Practice: Unleash the Science of Learning

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Harness powerful teaching strategies, unleash the science of learning, and transform your classroom with resources by cognitive scientists.

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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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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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Creativity, insight and "eureka moments," with John Kounios, PhD

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In my FLC today we discussed metacognition, and Donald Rumsfeld's quote, "...there are things we don't know that we don't know". As John mentioned in his post, students may complete an exam confident that they will do well, and are shocked when they don't.

How about us instructors? We may teach our students, and are confident that they received the information, only to find that their exam scores don't reflect this. This is where peer observation can be valuable.

The connection we perceive we have with our students may not necessarily be the connection we actually have. A peer observing us has a perspective we lack by being able to see the connection from a distance, and share with us any disconnect that she/he can identify.

For example, it is very important to be passionate about what we teach. Students pick up on our enthusiasm, and it can be inspiring. But this very passion may blind us: swept up in the moment, we can assume the students are connecting the dots just like we do, and they may be nodding their heads and even smiling, while still not making the connection. It's easy to forge ahead without waiting for student feedback, so we miss the opportunity for assessment.

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In my FLC today we discussed metacognition, and Donald Rumsfeld's quote, "...there are things we don't know that we don't know". As John mentioned in his post, students may complete an exam confident that they will do well, and are shocked when they don't. How about us instructors?

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John Kounios - The Eureka Factor: Aha Moments, Creative Insight and the Brain

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Aha! Moments: The art & science of breakthroughs | Ina Catrinescu | TEDxTirguMures

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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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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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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.