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

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

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

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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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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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The surprising habits of original thinkers

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

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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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Feats of memory anyone can do | Joshua Foer

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Every student deserves a great teacher. So how do we develop as many expert teachers as possible? This would help every child. To truly address equity in education, to address disparities in learning outcomes for millions of students in the United States, many of whom are Black, Indigenous, and people of color, developing expert teachers is an imperative.

"The strongest education research finding in the last 20 years is that the quality of a teacher is the single greatest in-school determinate of student outcomes," writes David Steiner, executive director of the Johns Hopkins Institute for Education Policy and former commissioner of education for New York state, in a 2018 policy brief. It impacts job prospects, lifetime earnings, social skills, reduced risk of adverse life events, as well as academic outcomes-and these impacts are greater for students of color. At The Center for Transformative Teaching and Learning (CTTL) at St. Andrew's Episcopal School (MD), we believe that any plan for addressing equity in education needs to include ways to increase teacher effectiveness.

Research suggests that years of experience does not correlate with expertise or effectiveness. While this might at first seem depressing, there is a plus side. We don't have to wait years to develop expert teachers-there are strategies, knowledge, and attitudes we can target in professional development to make teachers more effective atany stage of their career. In professor John Hattie's research on the difference between experienced and expert teachers, he found that the main distinguishing factor was the ability to come up with a good strategy in the moment to help a child or group-and to do so with an automaticity and enough mental resources to handle everything else happening in their class at the same time.

Developing each teacher's toolkit of strategies and helping them learn to use the right strategy at the right time is critical. And must include strategies grounded in diversity, equity, and belonging (DEB), as well as the science of teaching and learning or mind, brain, and education (MBE). Together, DEB and MBE are a powerful combination for great teaching and make learning more equitable-and better forevery student, not just people of color, not just students with learning differences. Though students of color and students in other marginalized groups are impacted even more and often in different ways.
Belonging Together
It would be timely to illustrate this DEB + MBE approach by looking at belonging, which is starting to move center stage in educational discourse right now. Research on fostering a sense of belonging suggests it has tremendous potential for improving school performance and outcomes for students of color and first-generation students, but we are just beginning to scratch the surface of what this really looks like in schools.

One key element is setting high expectations and communicating to students-verbally and nonverbally-that we believe in their potential to meet these expectations over time-and then following through as a teacher to help make this happen. What does this take? Part of it is creating a classroom environment where each student feels safe, heard, known, respected, appropriately challenged, and that their voice, experiences, and culture matter. Supporting identity validation and eliminating identity threat are part of it as well. And so is knowing the research on metacognition, feedback, cognitive load, effective and efficient study strategies, assessment, and homework-and then translating it into everyday classroom actions. DEB provides one useful lens and toolkit of strategies to help us do this; MBE provides another. Neither DEB or MBE are sufficient on their own; together, they can help us create school and class environments that support each student on their learning journey. Another way of looking at it is that MBE can be used in support of DEB goals.

We are talking about a holistic science of teaching and learning that helps each child first feel they belong and then feel they can flourish. We need to start seeing DEB and MBE as intersecting, complementary fields, and to shift teacher training accordingly. DEB + MBE gives teachers the best possible knowledge, language, and strategies to deal with the hundreds of decisions they will make each day.

Teachers and school leaders must consider the everyday life of a student at their school:

Do they feel safe in every space at school?

Do they feel valued and that their experiences, culture, and unique story matter?

Do they feel like they can bring their full self to each class? Do they feel known? Do they feel like they belong?

Do teachers have high expectations for every student? Do teachers believe in every student's capacity to meet these expectations over time with their help? Do they communicate these beliefs to students regularly? And do teachers believe themselves when they say it?

In class, how does the teacher speak to students? Are all students given the benefit of equally long wait time when asked a question?

Is classroom instruction engaging, accessible, and relevant? Is it presented in alignment with what we know from cognitive science about how the human brain learns?

What does practice look like-does it help students develop effective and efficient strategies?

Does the feedback they receive help or hurt-and take the emotional experience of receiving feedback into account? What about homework?

Is there formative assessment before summative assessment, and do teachers tweak their plans based on what they learn from it?

The first part of this list is more connected to DEB; the second part is more connected to MBE. But in the child's reality of their day, all these pieces combine to create powerful, deep-seated feelings about school and a perception of whether school is a place for them and a valuable part of their life journey, and whether they will bring their full self to school. In teacher training and professional development, DEB and MBE are separated; in practice they are intermixed.

There are many interwoven pieces in each child's story, and many things can make learning challenging. Neither DEB nor MBE alone gives us the complete picture or the full set of strategies to help support them to move forward. Dedicated DEB practitioners and professional development are vital for every school and every teacher-we desperately need to invest in and deepen this work. When we do this, and then combine it with the best of what we know about pedagogy, assessment, metacognition, and the like, we end up with something more powerful.
The Intersection of DEB and MBE
There are many ways that DEB and MBE interconnect. We illustrate some of the ways in a Venn diagram (see "At the Intersection" below). We chose a Venn diagram because it is not hierarchical-the middle section is not better than the outside sections; it is just where they intersect. DEB and MBE are both research-driven fields-one is not "more scientific" than the other. Equitable teaching includes all of the bullet points listed and many more. There isn't space to include everything that is important.

What does this intersection look like in practice? As a first step, standalone DEB work needs to be firmly embedded in the core of our schools to eliminate inequitable practices and policies and to train all educators to deepen their practice in culturally responsive teaching. But what next? Many needs, challenges, and opportunities in schools can be better addressed by applying a DEB + MBE toolkit. For example:
When faculty are training to deliver beloved content knowledge in ways that make it more durable, usable, and flexible, we need to consider MBE principles like spaced practice, retrieval practice, and knowledge transfer. But we also need to consider DEB principles, such as a sense of belonging, what it means to make learning relevant, and equitable access to knowledge, resources, and support during project-based learning.

We must learn best practices for giving students feedback and pay particular attention to the emotional climate of how it is received, which determines whether the feedback will have a positive or negative impact. This starts with making it a priority to know each child and deliberately working to build strong relationships and a safe, calm, predictable classroom climate.

When we are working to build each student's sense of self-esteem, belonging, and self-efficacy, we need to make sure that we are teaching them the best possible study strategies and building their metacognitive ability to use the right strategy at the right time. These strategies tend to feel harder, especially at first, so they must be incentivized and embedded in each course, alongside discussions about belonging.

When we work to revise curriculum through a culturally responsive lens, we need to remember how memory is formed. New knowledge is always learned in the context of existing knowledge; what we know already is crucial in helping us learn what we need to know next. Be aware of the knowledge that your students and families bring to the school. At times we may have to "unlearn" things we have absorbed about history, race, gender, and so on before we can learn the more complete story.

We also need to plan curriculum backwards. Start with learning goals or competencies for the year, then, moving backward, plan the sequence of knowledge- and skill-building steps that have to happen in order to reach these goals.

We must address gaps in literacy-it's an equity priority-by choosing programs and training with a research-informed lens. "The reading war" about the best way to teach literacy is phony; general consensus exists on core principles, such as the role of decoding, fluency, and comprehension and the importance of structured phonics and a knowledge-rich curriculum.

When we apply both MBE and DEB strategies, we need to have an approach of "do no harm, assume nothing, and include student input." The right to get that input has to be earned through relationship-building-which is done patiently, over time, involving all teachers who share the same core mission. We cannot get students to buy into a DEB or an MBE strategy or approach without a sense of belonging or trust.

Where to Begin

Although belonging is becoming more of a prominent idea in our schools, but it is neither the starting point nor the ultimate goal. Positive social and academic identity formation, which for every student includes many constantly evolving strands, is the goal. This is what we celebrate when the student walks across the stage on graduation day-all the ways we've seen them grow in the time we've known them and all the possibilities of who they are becoming.

In our work at the CTTL, we've developed one possible guide that we think offers a promising path for DEB+MBE growth (see "A Guide for DEB + MBE"). It starts with the foundational work of teachers examining their own identities, inviting and creating space to learn about the identities of their students, and working to build a climate of safety and trust. Once this work is under way, teachers must keep at it and also work purposefully at building a sense of belonging in their class or school (which at times will involve cycling back to the steps below it). At every level, it's crucial pull in research-informed strategies from both DEB and MBE to help. For example, to help build a sense of belonging, casting a critical lens on pervasive narratives by exploring "whose voice is missing?" (a classic DEB strategy) and giving time in class for students to understand and act on feedback they have just received (a classic MBE strategy) are both helpful-and there are many others.

The challenges facing students and schools are complex and multifaceted, and benefit from multifaceted approaches to finding solutions. We need teachers and school leaders who can look at the Venn diagram and see how all the pieces fit together, how they all work in conjunction to help shape each student's experience at school. Great educators can do this-and do it in a way that leaves them with enough mental resources to deal with everything else that is happening in a busy school at the same time. By taking a DEB + MBE approach, we are more likely to create policies, practices, and places that better serve the needs of all students. It is a more robust approach to fight for equity in schools.

Readings and Resources

"The Secret of Effective Feedback" by Dylan Wiliam in Educational Leadership, April 2016

How to Give Effective Feedback to Your Students by Susan Brookhart

Belonging Through a Culture of Dignity: The Keys to Successful Equity Implementation, Floyd Cobb and John Krownapple

"Implications for Educational Practice of the Science of Learning and Development," Applied Developmental Science, Linda Darling-Hammond, Lisa Flook, Chann Cook-Harvey, Brigid Barron, and David Osher

Culturally Responsive Teaching and The Brain: Promoting Authentic Engagement and Rigor Among Culturally and Linguistically Diverse Students, Zaretta Hammond

"Teachers Make a Difference: What is the Research Evidence?," John Hattie

"Can Professional Environments in Schools Promote Teacher Development? Explaining Heterogeneity in Returns to Teaching Experience," Educational Evaluation and Policy Analysis, Matthew A. Kraft and John P. Papay

"Stereotype Threat and the Intellectual Test Performance of African Americans," Journal of Personality and Social Psychology, Claude M. Steele and Joshua Aronson

"Cognitive Ability and Teacher Efficacy," David Steiner

"Breaking The Cycle Of Mistrust: Wise Interventions To Provide Critical Feedback Across The Racial Divide," Journal of Experimental Psychology: General, David Scott Yeager, Valeria Purdie-Vaughns, Julio Garcia, Nancy Apfel, Patti Brzustoski, Allison Master, William T. Hessert, Matthew E. Williams, and Geoffrey L. Cohen

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A Powerful Combo: Diversity, Equity, and Belonging and Mind, Brain, and Education

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Every student deserves a great teacher. So how do we develop as many expert teachers as possible? This would help every child. To truly address equity in education, to address disparities in learning outcomes for millions of students in the United States, many of whom are Black, Indigenous, and people of color, developing expert teachers is an imperative.

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The Science of Learning - How does learning transfer to new situations in/outside of the classroom?

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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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Research around the way humans learn is booming these days. Consider viral brain-based teaching trends and explorations of how the act of teaching shapes kids' brains.

Mariale Hardiman, vice dean of academic affairs at the Johns Hopkins School of Education and and director of Johns Hopkins' Neuro-Education Initiative.

But studying how the brain learns doesn't necessarily mean memorizing proteins and brain chemistry. Sometimes it's about empathy -or in the case of some of the latest research coming out of Johns Hopkins, it's about understanding how art plays a role in learning.

One person who has closely watched, and even shaped, the coevolution of neurosciences with education is Mariale Hardiman, vice dean of academic affairs at the Johns Hopkins School of Education. The education professor is also the co-founder and director of Johns Hopkins' Neuro-Education Initiative, a center that aims to bring together research on learning and neuroscience, teaching and education.

EdSurge sat down with Hardiman recently to learn about the Initiative' recent findings around how injecting art into lessons across disciplines can boost memory and retention. (This conversation has been lightly edited and condensed for clarity.)
EdSurge: I'd like to start with a bit of history about the program. What were you hoping to do with the program when it was first launched?
Hardiman: So it launched in 2007. The 90s was the decade of the brain. All these neuro fields were popping up- neuroaesthetics, neurolaw, looking at things like adolescent brain development. So much research came out in the popular media and it seemed that teachers were becoming interested. So I connected with people who run the Learning and the Brain conference and began to attend. And in my own doctoral work here at Johns Hopkins I became interested in looking at the research on neuro and cognitive sciences and what of that is relevant to educators. And how is this research informing how children learn and why should teachers be consumers this research.

(1) Hardiman worked in the Baltimore City Public Schools for more than 30 years before her role at Johns Hopkins. She was a principal at the time that she developing Brain-Targeted Teaching.

My first book was in 2003 (1*), and that book was all about connecting research with effective teaching. And in that book I launched what I called the Brain-Targeted Teaching model. At the time, our state test was based upon performance assessment, and learning the thinking skills framework. So based on that framework and other frameworks like Bloom's Taxonomy and multiple intelligences, I married those thinking skills frameworks with what I felt was really relevant from the neuro and cognitive sciences and created a teaching model to a translational model.

I came up with six areas that I called brain targets. When I was studying at Hopkins, my professor knew I was interested in this. What he said to me was, 'Brain-based learning is the silliest thing I've ever heard,' or 'Where else would learning occur? We don't think with our feet.'

And I looked at him and I said, 'Yes, brain-based learning is a silly way to describe it. However, while we know all learning is brain-based, we also know that all teaching does not result in learning.' I don't use the term brain-based learning. I focus on teaching targeted to how the brain thinks and learns. And that's how I came up with the name Brain-Targeted Teaching. I've been doing talks like this for 10 or more years.
What does Brain-Targeted Teaching look like in practice?
There are six components (2). The first is the emotional connection to learning, which has just exploded in terms of the research. Some of that is on the effects of stress on learning, and then also the effects of positive emotions and personal connections to learning. Social-emotional learning programs have proliferated-the idea that schools can be protective factors for students who are in toxic stress situations. A lot of intervention has been developed, like mindfulness training, restorative practices, et cetera.

The second component is how the physical learning environment-like light, sound or too much noise-can impede learning.

The third target is looking at the brain's propensity to use patterning and think holistically to design instruction. What are the standards that we want the children to know? How does that map into objectives? How are you going to evaluate it? And now let's start thinking through what activities we're going to do for mastery and for application.

The arts became a central part of this because I found that art teachers working with classroom teachers really helped them think through creative ways to plan those units.

(3) "Summer slide" is a term that refers to when students fall behind where they were academically over the summer months. Studies have shown that gaps in reading grow larger among low-income students over summer, while middle class students experienced gains between spring and fall.

Brain target four: What strategies are we using from the neuro and cognitive sciences to teach for mastery? And mastery means memory. You have to remember what you're learning or else you can't do anything like that. And we know that's a problem in education. Think about summer slide (3) right now. Many, especially vulnerable learners, seem to lose the most over the summer. I started looking at things like chunking, active retrieval, spacing and then, obviously, repetition.

As I thought about repetition, that's where the arts first started coming in. You don't want to teach something if you're going to repeat that content enough times that it sticks. You certainly don't want to keep teaching them the same way over and over again. That's going to be pretty darn boring for them. So how can the arts help to reinforce content, and then how can it help to teach it in the first place? And so we started embedding the arts into every Brain-Targeted Teaching learning unit.

Brain target five then is applying knowledge. When kids can demonstrate that they understand something, that they know something, you're done teaching when they can do something real with it. So what are the big-picture activities that you want the children to be able to do or apply in some way? That mirrors project-based learning.

Then six is evaluation, and lots of research around feedback and even student's' knowledge of when they're getting feedback and how that affects performance. Not assessing of knowledge but assessing for knowledge.
What areas of research is the center looking at right now?
We published two randomized controlled trials, one in 2014 and then the second one in 2019. Then we did a similar study where we randomized kids and wrote treatment and control units using the same science content and matching for duration of activities and dosage as tightly as possible.

Our research question was, Is teaching with arts integration the same as or better than traditional instruction? And the reason we said 'same as,' is that there are so many other benefits from the arts [that are] well documented, like student engagement, creative thinking and problem solving. So we were just testing memory [after] 10 weeks out, which is similar to the summer slide.

So every group of randomized kids got one unit with one condition, and then the reverse in a different content. So they may have had astronomy and arts integration for three weeks and then took a post-test and then went on to their second unit, which would have been in the opposite condition.
How was this being measured?
Very standardized. Just multiple choice. And interestingly, in both studies what we found was the real drivers of this are for students at the lower levels of achievement. In the second study, we broke students down into proficient and advanced, proficient and basic. Both times the kids at the lower levels of achievement really rocked it in terms of what they remembered in arts versus what they remembered in traditional instruction. For kids at the higher levels of achievement, there was no difference, really. So there was no harm done. But that makes sense because if you are an adept learner in the traditional style, then you don't need anything different to be an adept learner. You can learn the way your teacher teaches you. But if you have maybe a learning disability, you don't speak English as well as others or your achievement has not been the same, arts gives these children multiple ways to express what they know.

How do teachers respond to this? Have you ever run up against, say, a math teacher who doesn't want to incorporate arts into their lessons?

(4) Hardiman told the New York Times that "arts integration should not replace arts education." She instead suggest a three-pronged approach: arts education, arts and cultural offerings, like museum visits, and then integrating the arts into teaching other subjects.

That's a really interesting question. In fact, it is another study we're doing because we gathered a lot of data from the teachers. We're now studying the student results compared to how the teachers said they liked teaching the units. But we haven't published that yet. I can just tell you anecdotally we did see a teacher who just said, 'I really don't like to sing. I don't want to sing in front of my kids.' So, you know, we tried to help her out. If this were to be instituted, you'd just have it on CD or on your phone and play the song. You don't have to sing. But our art activities were simple enough (4) that it did not require teachers to have a degree in the arts at all. And we did training, so we walk them through the arts activities, so that they could do that in our training before the study.
You mentioned how the '90s was sort of this boom of brain science research. How has the conversation and research around neuroscience and education changed over the last few decades?

(5) Hardiman believes teachers should know some neuroscience basics. Does that mean they need to know every protein and mechanism that occurs in the brain? "No," she says. "But do they need to know that for the children in front of them, their brains are continuing to grow... Teachers' belief systems, what we call efficacy beliefs, is mapped to student achievement tighter than almost any other construct."

There's so much more acceptance. So many more people are writing books and articles. And you know, when I first started in this field and my, when my book first came out in 2003, I will say that there were a lot of people, especially in the field of education, not even in cognitive science or psychology, brain science [that rejected the idea]. It was education professors (5) who didn't want to have anything to do with this.
Could asking teachers to use these techniques backfire?
I think one area that we should be cautious of is that there's a lot of research on the effects of toxic stress. And a lot of the research was done with children in poverty. So the idea that children in poverty do experience more toxic stress than children who do not grow up in poverty.

So what I would worry about is if someone said, "These kids' brains are impaired [due to stress] [so] these children can't learn because they're in poverty, and their brains had been damaged." What I always say with that is that the brain is plastic. And can schools be protective factors and reverse any effects of stress? And the answer is absolutely yes. And that's where we get into the strategies.

(6) Some education experts are more skeptical of SEL. Chester E. Finn, Jr., a fellow and president emeritus of the Thomas Fordham Institute, recently wrote: "We see a clear and present danger that SEL could go off the rails in any number of ways," if not implemented properly.

So I would never want to give them research without saying, Here's what you can do as a protective factor. That's also why the SEL [social emotional learning] (6) stuff is important because what we're learning is one of the most important protective factors for kids is a relationship with a caring adult in a school building.
What else are you paying attention to that's happening in this field right now?

(7) Federal funding for arts in education has experienced major cuts in the last decade, going from $40 million in 2010 to $26.9 million in 2018.

What I'm most excited about is could the arts be a driver of school reform instead of a victim? (7) Could the arts actually help to close the achievement gap?

Nobody knows quite how to close the achievement gap, and I think that the arts could be at least one answer. And that's what I want to do. I want to keep studying this and seeing whether or not we can have all students learn at high levels and just be excited about learning, and the arts do that.
*Footnotes can be viewed in the desktop version of this article.

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This Neuroscientist Wants to Know Your Brain On Art-and How It Improves Learning - EdSurge News

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Research around the way humans learn is booming these days. Consider viral brain-based teaching trends and explorations of how the act of teaching shapes kids' brains. But studying how the brain learns doesn't necessarily mean memorizing proteins and brain chemistry.

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New research reveals that the arts may prime our neural circuitry for a broad range of activities, boosting crucial cognitive and social skills like spoken and written language, focus, self-control, and empathy. In a 2016 study, for example, T. Christina Zhao and Patricia Kuhl demonstrated that babies exposed to simple melodies in a social setting developed a greater sensitivity to the rhythms of spoken language. More surprisingly, they noted, the processing of music was traced not just to the auditory cortex of the infants, but to the prefrontal cortex as well-the seat of higher-order cognitive faculties like attention and self-regulation.

"We can see that the babies who have been through the music experience have greater abilities to...hold attention when that's important, and to switch attention when it's appropriate to switch," Kuhl explained in a TED talk. "In other words, music is affecting executive function."

A 2019 study reached similar conclusions with professional musicians, finding that "executive attention is more efficient in musicians than non-musicians," and improves as musical training progresses.

But those weren't the only surprises in store for researchers. A major 2019 study tracked over 10,000 students in Texas as they participated in arts programs, concluding that they performed better on state writing tests, were better behaved, had more compassion for fellow students, and were more engaged in school. And a 2018 study showed that drawing had a dramatic effect on memory, outperforming writing, visualizing, and other retention techniques.

We also referenced other studies, reports, and news stories as we produced this video: A comprehensive 2017 American Institutes for Research study on arts integration; 2016 and 2014 research on executive function; a 2013 study on the value of field trips to museums; a 2019 news story on the recent discovery of ancient cave paintings; along with the following older research: a 2009 report; this 2011 research; this 2008 research; and this 2004 research, among others.

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New Studies Link the Arts to Crucial Cognitive Skills

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New research reveals that the arts may prime our neural circuitry for a broad range of activities, boosting crucial cognitive and social skills like spoken and written language, focus, self-control, and empathy. In a 2016 study, for example, T.

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Focus: Attention Science

Abstract

New and evolving technologies provide great opportunities for learning. With these opportunities, though, come questions about the impact of new ways of acquiring information on our brain and mind. Many commentators argue that access to the Internet is having a persistent detrimental impact on the brain. In particular, attention has been implicated as a cognitive function that has been negatively impacted by use of digital technologies for learning. In this paper, we critique this claim by analyzing the current understanding of the cognitive neuroscience of attention and research in educational settings on how technologies are influencing learning. Across the two bodies of literature, a complex situation emerges placing doubt on the claim that the use of digital technologies for learning is negatively affecting the brain. We suggest therefore that a more systemic approach to understanding the relationship between technologies and attention involving researchers examining the relationship at different levels from the laboratory to the real world.

Keywords: Attention, learning, educational technology, memory

The Role of Attention in Learning in the Digital Age

The means and media through which people can learn are fundamentally changing. Technology increasingly impacts on the ways in which people acquire, update, and correct their understanding. The emergence of mobile networked devices now means that information can be accessed anywhere, anytime with a connection to the Internet. This new information reality has created substantial affordances for learning both in formal education and in informal settings. These opportunities have seemingly not come without a cost. Many scholars and commentators [ e.g. 1- 3] argue that the ease with which we can now access information is negatively and persistently impacting our capacity to learn, understand, and interact with others. In particular, attention is implicated as a key factor in the apparent negative influence of technology on learning in the digital age. Our aim in this paper is to critically evaluate the claim that technology is simply causing ongoing attention deficits that can be traced to neurophysiology. As we will demonstrate, the situation is vastly more complicated than it may seem on the surface.

The impact of emerging technologies on various mental functions has been a topic of some consternation for decades, if not centuries. For example, the invention of the printing press led to much speculation about how much of a negative impact the wide distribution of books and other print material would have on memory [cf. 4]. The concerns about the influence digital technologies seemingly have on people has increased in recent years. For example, Baroness Susan Greenfield has been making claims that digital technologies are having a negative impact on our brains for some years [ e.g. 5, 6]. These concerns have been reflected in the writing of many others, including journalists [ e.g. 2] and other commentators [ e.g. 7]. The concerns span possible negative impacts on everything from attention, to memory, to capacity for social interaction. It appears, therefore, that there is a growing tide of concern about the detrimental effects of digital technologies on everything from foundational cognitive functions through to more complex social dynamics.

In the research literature, the claims about the effects of digital technologies on learning, attention, memory, and social interaction are more nuanced than those in the popular media but point to inconsistencies and contradictions. For example, Loh and Kanai [ 8] critically examined the evidence of the impact of the Internet on the brain in the neuroscience literature. They found that, although there are some examples of neuroscience studies pointing to changes in the brain as a result of Internet use, the evidence is far from conclusive. Within an educational setting, Junco and Cotton [ 9] conducted an analysis of the effect of digital multitasking on student learning in a formal educational environment. The results of their study show robust evidence of the negative impact of multitasking in digital environments on academic performance (an issue we will return to later). The more students engaged in multiple activities in digital environments, the worse their academic performance. Furthermore, there is one area where there has been a substantial body of integrated research examining the influence of technology: reading and comprehension [for overview, see 10]. There is a growing literature on comparisons between reading in a digital environment and reading from printed material. In a review and meta-analysis of this research Delgado and colleagues [ 11] found a robust disadvantage to reading in a digital environment, otherwise known as "screen inferiority" [see also, 12]. Several mechanisms for this effect have been implicated, including the possibility that digital environments are inherently more distracting with scrolling and other aspects of the digital experience taking away cues and critical cognitive processing capacity from the task of comprehending the material [ 13]. The literature on the screen inferiority effect suggests that there may be robust evidence of the negative effects of technology on learning. However, the bulk of the research on screen inferiority has been conducted under controlled laboratory conditions. The conclusions of the meta-analyses by both Delgado et al. [ 11] and Kong, Seo, and Zhai [ 12] suggest that there are many complicating factors, such as the nature of the content, the amount of study time allocated, and demographic factors that could influence the effect outside the lab. As such, it remains uncertain as to when and how technologies influence reading and comprehension and what mechanisms might be responsible. Across the research literature, there are therefore some indicators of the possible negative implications of technology use for learning, but many unanswered questions remain about the implications in the real world.

In order to provide an overview of the impact of technology on learning, Tamim, Bernard, Borokhovski, Abrami, and Schmid [ 14] conducted a second-order meta-analysis on 40 years of research on the role of technology in learning in educational settings. The analysis revealed that the overall effect size of technology use on learning is Cohen's d = .33, suggesting a positive influence of technology on learning in classrooms. However, this effect size is below that recommended by Hattie [ 15] of d = .4 based on his very large second order meta-analysis. An effect size of .33 suggests that the use of technology to enhance learning might be positive overall but not among the most effective ways of enhancing learning. So, while there is much discussion about the potential negative impacts of technologies, there are few examples of robust research findings across different levels of analysis to support this claim, particularly at a physiological level. Aside from the aforementioned research on reading and comprehension in digital environments, much of the emphasis of the research has been on the impact of technologies on learning in the classroom. The research that has been conducted in these contexts is inconclusive, demonstrating both benefits and harms in the use of technology on learning, attention, and memory. Selwyn [ 16] describes the polarization of the views in the literature on educational technology as the "booster" and "doomster" positions. Even though the research literature points to some uncertainty about the impact of technologies, there is a general tendency for commentators to see technology as either a savior or as a direct path to a dystopian future.

Determining how technologies might be impacting broader cognitive functioning, such as that involved in reading and comprehension, and neurophysiology "in the wild" is a complicated undertaking. There are two main reasons for this. Firstly, laboratory-based research in cognitive neuroscience cannot easily be translated into the real world to provide any confidence about possible cause and effect relationships between technologies and the brain [ 17]. While lab-based research gives some indication about how certain technologies might influence the ways in which people deal with and acquire concepts and ideas under controlled conditions, the capacity to infer from such constrained conditions to the complex real world is limited [ 18]. Social and environmental factors will heavily influence how these processes occur and how effective they are for people in their day-to-day lives. As technologies become more ubiquitous over time, the skills needed to use the technology become more important than the skills the technology supplants. For example, as calculators and computers have become readily available, the need to carry out hand calculation has diminished. There may be clear differences evident in neuronal activity as people do computer calculation or hand calculation, but these differences mean little in a world where calculators and computers are so commonly used. The translation process from the laboratory to the complex real world where these factors need to be considered is therefore difficult. Correspondingly, the ability to make any claims about whether technology is causing persistent, high-level changes in neurophysiology in a meaningful way or not on the basis of data collected in the wild is similarly limited. The complexity of the situation makes any correlation between factors difficult to interpret in isolation. This includes the specific impacts of different technologies.

A second, related issue is that, given the interaction of factors contributing to the ways in which people work with information in the 21 st Century, it is difficult to isolate certain factors both in terms of the specific technologies people use and how, but also the specific cognitive functions they ostensibly influence. This has proven to be the case in research on screen superiority, for example. The net result is that broad claims are made about the impact of technologies on the brain without any specificity about the exact technology that is causing the problem and under what conditions it is an issue. There is then also a range of factors that are implicated as the point of influence. Some point to the negative impact of digital technologies on motivation, self-regulation or shallow "engagement" [ e.g. 2]. Others make more specific claims about aspects of attention [ e.g. 19] and/or memory [ e.g. 20]. The influence of technology is then also extended to the physiology of the brain by others still [ e.g. 6]. A not insignificant part of the problem of determining whether digital technologies are having a negative impact is that there are many technologies implicated and many places where the technology is having an impact, which ultimately highlights the inherent complexity of the problem.

Beneath all the complexity described here, there is one cognitive function that is consistently featured as the primary place where technologies are having a negative impact: attention. In an early attempt at understanding how attention might be influenced by technology, Hembrooke and Gay [ 21] examined differences in memory performance between a group of students allowed access to laptops during a lecture and a group that did not have access. They found a substantial decrement in performance for the group with access to laptops and attributed this poorer performance to the splitting of student attention in this group. In one of many similar studies, Wood and colleagues [ 22] separated off-task ( e.g. social media use) and on-task ( e.g. note-taking) activities to determine how much of the negative effect of technology is simply due to doing two things at once. They found that the groups allowed access to social media, texting, and email (off task activities) performed poorly compared to those engaged in task-related activities. They use these results to argue for the substantial detriment caused by the use of digital technologies over and above simply doing more than one thing at a time. This conclusion shares a family resemblance with results from neuroscientific studies. For example, Foerde, Knowlton, and Poldrack [ 23] found a decrease in performance accompanied by correlated differences in activity in the medial frontal cortex for participants completing dual tasks, as opposed to a single task. In summary then, it is apparent that there is some merit in the argument that attention is being negatively impacted through the use of technologies with some evidence that there are associated neuronal correlates. Much of the evidence to support this claim, however, is specifically focused on the detrimental effects of multitasking in highly controlled experimental settings looking at isolated parts of the brain or in highly complex educational contexts [see also, 24]. The body of research therefore does not provide evidence that the impact is occurring at a biological level in the global and persistent manner that is described by those taking the doomster position on technologies. In essence, the problem that is observed in real life learning situations is distraction and the negative consequences of constant task-switching. These negative consequences on working and long-term memory have been particularly evident in habitual multitaskers [ 25]. What remains unclear then is exactly how attention is implicated here, what is occurring in the brain over the longer term, and what can be done to address this distraction. To delve into this, we now turn to what is understood about the cognitive neuroscience of attention.

The Cognitive Neuroscience of Attention

Probably the most critical element of attention that is relevant to how information is processed in digital environments is its restricted capacity. Humans have only limited neural resources to process the complexity of the surrounding environment. Moreover, there are an infinite number of ways in which we could act in any given situation at any given time. The cognitive ability to allocate our attention selectively allows us to prioritize only some elements of the environment while filtering out others. A now classic example of such filtering is known as the "cocktail party effect" [ 26, 27]: when standing in a room full of people speaking to one another, relatively little effort is required to tune into only a single speaker of interest. In such an instance, the selected speaker can be understood easily while all surrounding conversations turn into incoherent background noise. This phenomenon, selectively attending to only a single auditory source amongst many, demonstrates the cognitive capacity to voluntarily filter information according to our internal goals. In some cases, however, our attention is captured involuntarily. Consider again selectively listening to only one speaker at a cocktail party, but, seemingly from out of nowhere, you hear your name being spoken by someone you had previously been ignoring. Auditory filtering would automatically shift to tune into this new speaker, making their conversation clear while the previous speaker's words become incoherent. Thus, although attention can greatly focus our thoughts and actions on only some aspects of our environment, the ways in which we allocate our attention depend on both our internal goals as well as external factors.

Both voluntary and involuntary forms of attentional allocation greatly impact many other cognitive functions [ 28]. Visual working memory, for example, is the ability to hold in mind visual information, such as simple shapes, colors, or letters, for just a few seconds. Visual working memory provides a sort of cognitive buffer that temporarily stores perceptual objects during decision making and action planning, and is highly predictive of intelligence [ 29]. However, this form of memory is surprisingly constrained; countless lab-based experiments have revealed that as more items are required to be remembered, the resolution with which those items can be remembered decreases dramatically [ 30]. Indeed, a classical view of visual working memory is that only three or four items can be remembered at one time, and any additional information is simply not stored in memory [ 31]. Given this highly limited capacity to hold items in memory, attentional control can play a critical role in governing whether a subset of visual information should hold priority in working memory. If a particular visual object is expected to be more important than others, voluntarily allocating attention to that object improves the precision with which it is remembered, but this improvement comes at a loss of memory precision for non-attended objects [ 32]. Indeed, the neural resources involved in holding items in visual working memory appear to change dynamically according to attentional goals. Attentional distraction, therefore, can result in the inability to hold information in memory, even for short periods of time.

Although the picture is still incomplete, the brain areas involved in attention have been thoroughly investigated over recent decades. A large distributed network of brain areas is involved in attentional control, primarily extending between so called "parietal." "temporal," and "frontal" brain areas [ 33]. Components of this network are differentially engaged when preparing and applying voluntary attention versus involuntary attention. Unsurprisingly given the important role of attention in guiding decisions and actions, the neural areas involved in attention are heavily connected to brain areas involved in processing basic sensory signals and the planning and generation of motor actions. When voluntarily allocating visual attention to a particular area of space, the attentional network sends signals that enhance the responsiveness of the brain areas involved in processing information at that location [ 34]. Similar changes in neural activity occur when attention is allocated involuntarily, though the precise areas of the brain involved in involuntary vs. voluntary attention are at least somewhat dissociated [ 33].

Damage to the attention network can result in remarkable attentional deficits. For example, following a stroke in the right side of the parietal cortex, a brain area particularly involved in the voluntary allocation of attention, the stroke patient can lose awareness of visual information toward the left side of space. As a result, the patient may only eat food on the right side of a plate, and fail to read words on the left side of a page [ 35]. This phenomenon, referred to as "neglect," demonstrates that the brain areas involved in attention are critical to even basic sensory experiences.

It is not just injury, however, that can influence attention to the extent that there is a substantial loss of awareness; a perfectly healthy and typically operating brain is also susceptible to gross lapses in attention. Indeed, a skillful illusionist explicitly manipulates an audience's attention so as to conceal how a magic trick is accomplished [ 36]. Attention is also involved in cognitive illusions, such as "change blindness." In a change blindness experiment, dramatic changes in a visual scene will go completely unnoticed if an observer has their attention disrupted for just a fraction of a second [ 37]. This effect of distraction is so robust that, for example, the observer will fail to notice that an airplane's engine is disappearing and re-appearing in consecutive images, when those images are separated by an attention-disrupting brief flash. Furthermore, attention can be greatly impacted by even more subtle distractions depending on internal goals. During a landing procedure in a flight simulator, pilots who had information continuously presented digitally on their cockpit often failed to notice a clearly visible plane on the runway, resulting in a collision event [ 38]. Therefore, focusing attention on some information can come at the cost of our attention not being captured involuntarily, even when such a shift in attention may be ideal.

One of the primary ways in which humans shift their attentional focus is by making eye movements. The most common type of eye movements, called saccades, rapidly shift high resolution central vision to a point of interest [ 39]. Central vision, as opposed to lower-resolution peripheral vision, is the only part of the visual field that has the visual resolution necessary for many common visual tasks, such as reading, recognizing faces, or watching television [ 40]. Humans thus make approximately two to four saccades per second to move objects of interest into central vision, resulting in a greater attentional focus for those objects than for objects in peripheral vision. Eye movements therefore provide an index of attentional focus, and the neural systems involved in eye movement control heavily overlap with those involved in both voluntary and involuntary attention shifts [ 33]. However, predicting where people will move their eyes, which is an overt shift of attention, remains one of the principle challenges in visual neuroscience [ 41]. Although recent advances have been impressive, the most up-to-date fixation models, which use a form of artificial intelligence to predict where a person will move their eyes, fail to make correct predictions for a substantial number of images [ 42]. This difficulty in predicting to what elements of an image a person will attend is particularly important in extending findings from typical cognitive experiments that use relatively minimal and contrived visual environments to scenarios beyond the lab. When predicting overt shifts in attention, researchers use naturalistic images, including, for example, outdoor scenes, people, and text [ 42]. The balance of successes and failures of models to predict where people will shift their attention in such complex scenarios attests to the difficulty in understanding the dynamics of voluntary and involuntary attention shifts in everyday settings.

Attention and Technology

It is evident in what we have presented thus far that attention is becoming better understood as a cognitive process from a neuroscience perspective. As we have explained, there is difficulty in determining how the biological processes are influencing and being influenced by technologies. This is particularly the case given the complex network of brain areas that are responsible for attentional control. It is therefore difficult to point to exact locations where long-lasting changes in information processing are impacting the brain. We can, however, see some evidence of the basic attentional processes observed in the laboratory environment also in play in real world settings. Drawing on the examples we have described here, there is a role of both voluntary and involuntary attentional processes for various technologies in the learning process. For example, if a learner is using the Internet to search for information about a given topic, they will be exposed to elements within that environment that will capture attention involuntarily. Pop-up elements of webpages are an obvious example of this. The voluntary engagement with material can be interrupted by elements in the environment that have been specifically designed to capture involuntary attentional processes. Herein lies one of the key areas in which technology has been deliberately created to exploit these processes.

Many websites, particularly commercial websites and social media, have been deliberately designed in order to capture attention and to maintain attention on that site [cf. 43]. The premise is that no exposure is bad exposure and therefore, the longer someone remains on a page, the more impact that page is having and the more likely it is that companies will be able to make a sale, either directly or through advertising. The underlying theory is based on mere exposure effect [ 44]; the more exposure a site or product gets, the more likely it is that it will be perceived favorably. The end result for someone trying to learn something by searching for information on the Internet is that there is constant competition between the voluntary attentional processes working towards the goal of greater understanding and the involuntary attentional processes constantly being lured away by design features specifically created for the purpose of attracting attention. Automated "notifications" are specifically designed to attract a user's attention to a new email, private message, or social media comment, necessarily drawing their attention away from some other task [ 45].

As has been described by Fogg [ 43], technologies have been specifically designed in order to attract and maintain attention on a site or in an application. Fogg coined technologies designed to exploit cognitive systems in this way as "persuasive technology." Between the concerns of technology insiders who have pointed out the ways in which technologies have been developed [ e.g. 46] and the doomsters who argue that technology is leading us into dystopia, it would be easy to point to the impact of technologies on attention as the central problem.

Several recent studies demonstrate that technology use can have either positive or negative effects on cognition, depending on the type of technology, context, and cognitive functions being examined. In a groundbreaking study, Green and Bavelier [ 47] studied whether the attentional demands of modern video games may improve video gamers' attentional capacities. They compared individuals who spent more than several hours a week playing video games to non-gamers and found that video gamers had superior attentional abilities on several standard cognitive tasks, such as ignoring distracting information, and attending to information over time. However, Boot et al. [ 48] repeated this experiment using a broader range of cognitive tasks than tested previously and concluded that it remains unclear to what extent attentional differences between gamers and non-gamers are due to pre-existing group differences, or to video game play specifically. Moreover, and as discussed above, evidence linking any positive benefits of such technology use to situations beyond the specifically trained task of video game playing is lacking [ 49].

The influence of media multitasking on cognitive ability is also unclear, and again likely depends on specific use cases as well as cognitive tasks being assessed. Ophir, Nass, and Wagner [ 50] developed a media multitasking questionnaire that distinguishes heavy media multitaskers from light media multitaskers, to test the hypothesis that media multitasking may train the ability to hold items in short term memory, switch between tasks, and ignore distractions. Contrary to these predictions, however, they found that heavier media multitaskers performed worse at a battery of cognitive tasks than light media multitaskers. A more recent study found that the mere presence of a mobile phone, but not its use, can reduce cognitive capacity [ 51]. Such media multitasking has been implicated in poorer learning in the classroom [ 52]. Although these studies may seem cause for concern about the growing abundance of devices on which to consume media, a more recent study called into question the conclusion that heavy media multitasking, as defined by Ophir et al., interferes with cognitive ability. Wiradhany and Nieuwenstein [ 53] repeated the same experiments as Ophir et al., with a different participant group. These more recent authors also performed a meta-analysis of published results, which is a statistical analysis that combines data from multiple studies thus improving the precision of the result over and above any single study. They found little support for the conclusion that heavy media multitasking negatively impacts cognitive performance as tested in the lab. Even when similar tests are conducted in relatively tightly controlled laboratory conditions, therefore, a clear impact of technology on cognitive performance remains elusive. Orben and Przybylski [ 54] note that, when testing the association between psychological well-being and technology use in a very large dataset ( i.e. over 300,000 samples), multiple contradictory conclusions can be made depending on how a single dataset is analyzed.

The reality is that attention is a complex process that interacts with perception, memory, and conscious experience. It has voluntary and involuntary components and can be influenced by factors such as interest, motivation, and self-regulation. The evidence from cognitive neuroscience about the diffuse activity associated with attentional control supports the inter-relationship between processes. The way we direct our attention will influence how we learn from technologies as much as the technologies can influence how we attend to them. There is therefore a complex two-way relationship between humans and technologies that is influenced by a range of other factors. For example, there is clear intent in the design of technologies towards certain ends, often to sell products or services. Focusing on learning and education specifically, Clark and Mayer [ 55] argue that the critical element to the successful use of technologies in learning is to rely on solid evidence to inform the design and development of instruction. In this instance, the intent of the design is specifically to enhance learning. In understanding the relationship between attention and technology use, factors such as the intention of the design must be taken into account, it cannot be assumed that technologies are neutral [see also, 16]. With the extent of the complexity that these factors bring to the situation, it is premature to make blanket statements about technology causing attention deficits in the brain, as is so often the claim in the popular press.

The Underlying Difficulty of Understanding the Role of Attention in Technology-Mediated Learning

The volume of commentary on the apparent influence of attention in technology's role in the demise of attentional capacity might suggest that the matter is largely settled. The research discussed here on the impact of reading and comprehension in digital environments vs. in print [ 11, 12] also suggests that there is a strong case for that technology is negatively impacting learning. We have merely begun to highlight the reasons why the situation is more complex than it is often portrayed, particularly in regard to the role of attention. There does appear to be some relationship between our attentional systems and the evolving ways in which we acquire, use, and update our understanding in the 21 st Century and, indeed, the screen inferiority effect has proven to be robust. Having said that though, there is some way to go before the true nature of the role attention plays in the observed effects is clear. Compounding the problem of developing this understanding is that technology evolves more rapidly than the research can. There is a substantial lag between the introduction of new technologies and the publication and dissemination of research about the implications of using the technology [ 56]. For example, mobile tablet devices have been available and in use for nearly a decade now but research on the implications of these devices on learning and in education has only recently been published [ e.g. 57, 58]. The principles of multimedia learning described by Mayer [ 59] provide guidance for teachers and learners in the interim but the lag between use and evidence means it is difficult to provide specific recommendations about particular technologies until some years after they are adopted in practice.

Given the complexity inherent in determining the relationship between attentional processes in the brain and technology-mediated learning, the only real option is to consider the situation systematically. This means taking a holistic approach to translating the basic research to understand how it might be applied in the complex social environment and to carry out systems-based analysis where the complexities of the situation may be methodically included in the analysis. Jacobson, Kapur, and Reimann [ 60] argue that this kind of systems-based approach is the only viable mechanism for overcoming the problems in analyzing how factors such as the use of technologies for learning can be adequately explored in educational environments. Systems-based approaches could similarly help to provide more comprehensive answers about the impact of technologies on attention. These approaches are, however, inherently complex requiring collaboration between researchers and practitioners (particularly in educational applications). This kind of collaboration requires testing assumptions about what attention and learning are, what level of analysis is appropriate for answering what kinds of questions and how to effectively translate and make meaning of research that occurs in highly controlled environments for the complex real world [cf. 61]. Without this more systemic approach, there will continue to be sensationalist claims made without substantive evidence of the impact of technologies on processes such as attention.

Conclusion

While some commentators are making bold claims about the negative impact of technologies on attention and how this, in turn, impacts on learning, there is much still to be understood. Research in cognitive neuroscience is helping to advance what we know about how attention works in the brain but there is some distance between this foundational research and what happens in the complex real world. While researchers and theorists continue to try to bridge this gap, technology vendors are exploiting attentional processes to engage people with websites and applications and keep their attention on the page or in the app. Increasing our understanding of fundamental attentional processes and how they influence learning in the complex social world will allow educators to develop strategies and tactics for helping students to better manage their own attention. A systemic approach to bringing researchers and practitioners together to make sense of the evidence across multiple levels of analysis is the only viable way to develop a sophisticated understanding of how technologies are influencing the brain.

Author Contributions

Both authors contributed to the conceptualization, writing, and editing of this article. JML funded by a Special Initiative of the Australian Research Council (SRI20300015). WJH funded through a National Health and Medical Research Council Fellowship (NHMRC APP1091257).

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Focus: Attention Science: The Role of Attention in Learning in the Digital Age

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Focus: Attention Science New and evolving technologies provide great opportunities for learning. With these opportunities, though, come questions about the impact of new ways of acquiring information on our brain and mind. Many commentators argue that access to the Internet is having a persistent detrimental impact on the brain.

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Dr Lara Boyd - Public Salon: - Educational Neuroplasticity