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Cool resource for online learning!

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Virtually Inspired: Showcasing Online Learning Innovation

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

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I've been trying to remember the name of this foundation for a long time...thanks OLC!

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Skip to content

The Dana Foundation explores the connections between neuroscience and society's challenges and opportunities, working to maximize the potential of the field to do good.

As of 2022, Neuroscience & Society became our dedicated focus. It embodies our spirit of open inquiry, collaborative research, and multidisciplinary thinking in service of the well-being of all people.

Resources

Use these resources to learn more about neuroscience and society and to engage with others around topics important to the field. Search by format, audience, and intended use.

Explore Resources

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The Dana Foundation is a private philanthropy that explores the connections between neuroscience and society's challenges and opportunities.

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Good for instructors and students!

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We are cognitive psychological scientists interested in research on education. Our main research focus is on the science of learning. (Hence, "The Learning Scientists"!). Our Vision is to make scientific research on learning more accessible to students, teachers, and other educators.

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"75 digital tools and apps teachers can use to support formative assessment in the classroom" (2021)

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"75 digital tools and apps teachers can use to support formative assessment in the classroom" (2021)

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Another great list of tools! Also, I think I know the author...

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

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

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

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

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

Record audio and video

Create quizzes, polls, and surveys

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

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

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

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

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

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

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

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

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

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

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

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

13. Quizalize Helps you create quizzes and homework.

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

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

16. Survey Hero Designed to build questionnaires and surveys.

17. SurveyMonkey Helpful for online polls and surveys.

18. SurveyPlanet Also helpful for online polls and surveys.

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

20. Yacapaca Helps you write and assign quizzes.

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

Brainstorm, mind map, and collaborate

Present, engage, and inspire

Generate word or tag clouds

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

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

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

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

Get real-time feedback

Foster family communication

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

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

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

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

Keep the conversation going with live chats

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

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

Create and store documents or assignments

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

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

Learn more

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

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

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

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

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"Online Tools for Teaching and Learning" 

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"Online Tools for Teaching and Learning" 

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It's a giant list of online tools - excellent supplement to resources list from grad school.

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University of Massachusetts Amherst

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

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

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

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"The Science of Learning - How Does Transfer to New Situations In/Outside of the Classroom?" (

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"The Science of Learning - How Does Transfer to New Situations In/Outside of the Classroom?" (

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Not only an excellent video on transfer, but the overall collection is useful for exploring the science of learning when there's not a lot of time to do so. Need to evaluate all the videos for whether they'd be beneficial to students if shared with them.

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

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"The Value and Effectiveness of Feedback in Improving Students’ Learning and Professionalizing Teaching in Higher Education" (2016)

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"The Value and Effectiveness of Feedback in Improving Students’ Learning and Professionalizing Teaching in Higher Education" (2016)

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To replace the link I lost when I used this article during graduate work at PSU - it's still relevant!

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"Practice for Knowledge Acquisition (Not Drill and Kill)" (2010/2015)

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"Practice for Knowledge Acquisition (Not Drill and Kill)" (2010/2015)

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Although the article's information is a bit dated and geared more towards traditional, in-person K-12 instruction, there are some good ideas for how to bring practice into the classroom in a meaningful and purposeful way.

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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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Offers a definition, examples, teaching strategies, and how to show students the value of metacognition. Although this is a topic I already know a lot about (yay Irene Clark!), this source could be useful if I wanted to share ideas with colleagues or students.

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

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L2L Strategy - Metacognition | Arizona Site

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

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"The Truth About the Left Brain/Right Brain Relationship" (2013)

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"The Truth About the Left Brain/Right Brain Relationship" (2013)

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Good article on the neuromyth and how the brain actually functions - could be useful to share with students depending on their belief in the neuromyth.

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Sometimes ideas that originate in science seep out into the broader culture and take on a life of their own. It's still common to hear people referred to as "anal," a Freudian idea that no longer has much currency in contemporary psychology. Ideas like black holes and quantum leaps play a metaphorical role that's only loosely tethered to their original scientific meanings.

What about the idea that some people are more right-brained and others more left-brained? Or that there's a distinctive analytic and verbal style of thinking associated with the left hemisphere of the brain, and a more holistic, creative style associated with the right? Are these scientific facts or cultural fictions?

An infographic reproduced just last month at Lifehack.org, for example, promises to explain "why you act the way you do" by revealing "which side of your brain you tend to use more." An article at Oprah.com explains " how to tap into right-brain thinking." And decades of research using behavioral and neuro-scientific techniques do reveal fascinating and systematic differences across brain regions.

On the other hand, some recent headlines challenge the left brain / right brain dichotomy. One highly publicized paper, summarized at The Guardian, failed to find evidence that individuals tend to have stronger left- or right-sided brain networks. A new book by Stephen M. Kosslyn and G. Wayne Miller argues that the left / right brain divide is largely bogus, and should instead be replaced by a top brain / bottom brain distinction.

So while there's something deeply compelling about the clear-cut, right-brain versus left-brain classification (or is that just my left hemisphere speaking?), we have good reasons for skepticism. The real story, as you might expect, is a bit more complicated - but arguably more interesting - than the infographics and popular headlines seem to suggest.

To get a clearer picture of what we do and don't know about hemispheric brain differences in humans, I was fortunate to have an opportunity to interview a leading cognitive neuroscientist, Kara D. Federmeier, whose research focuses on language, memory and hemispheric asymmetries throughout the lifespan. Dr. Federmeier is a professor of psychology at the University of Illinois at Urbana-Champaign, where she's also affiliated with the Neurosciences Program and The Beckman Institute for Advanced Science and Technology. (And, full disclosure, she was also one of my first scientific mentors and co-authors.)
One idea that's often heard in popular discussions of psychology is that the left brain is the seat of language and more "logical," while the right brain is more creative. Is there any truth to this idea?

One problem with answering this question is that we would first have to agree on what "logical" and "creative" even mean. So let's consider a (relatively) more well-defined case: math skills, which are often taken to be part of what the "logical" left hemisphere would be good at.

There are different kinds of math skills, ranging from being able to estimate which of two sets of things has a greater number of items, to counting, to various types of calculations. Research shows that, overall, the abilities that make up math skills arise from processing that takes place in BOTH hemispheres (especially the brain area in each hemisphere that is known as the intraparietal sulcus) and that damage to either hemisphere can cause difficulties with math. A left hemisphere advantage for math is mostly seen for tasks like counting and reciting multiplication tables, which rely heavily on memorized verbal information (thus, not exactly what we think of as "logical"!). And there are right hemisphere advantages on some math-related tasks as well, especially estimating the quantity of a set of objects. This kind of pattern, in which both hemispheres of the brain make critical contributions, holds for most types of cognitive skills. It takes two hemispheres to be logical - or to be creative.

The claim that the left hemisphere is the seat of language, however, is a little different. That idea comes from observations that damage to the left hemisphere (for example, due to a stroke) is often associated with difficulties producing language, a problem known as aphasia. Similar damage to the right hemisphere is much less likely to cause aphasia. In fact, for most people, the left hemisphere does play a much more important role in the ability to speak than the right hemisphere does.

However, this does not mean that the right hemisphere is "nonverbal." My laboratory studies the hemispheres' ability to comprehend (rather than produce) language, and we, like others, have shown that both hemispheres can figure out the meaning of words and sentences - and that they have differing strengths and weaknesses when it comes to comprehending. So, like other complex skills, the ability to understand what we read or what someone is saying to us requires both hemispheres, working together and separately.

Early studies of hemispheric asymmetries often relied on "split-brain" patients who had the corpus callosum - the bundle of neural fibers that connects the two hemispheres - severed as a treatment for severe epilepsy. In such studies, information could be provided to a single hemisphere at a time by presenting people with input to one side of the visual field, since the right visual field is processed by the left hemisphere, and vice versa. Your lab uses contemporary neuro-scientific techniques, such as measures of brain wave activity (EEG and ERP) to investigate hemispheric asymmetries, and typically does so in individuals with intact brains. How do you do so, and do your findings corroborate or challenge earlier inferences made from the behavior of split-brain patients?

We actually use the same basic technique, known as "visual half field presentation."

As an aside, I should point out that many times people misunderstand and think that each EYE is connected to a different hemisphere. That's not true. (It would make our studies so much easier if it were, since we could just ask people to close one eye!) Instead, half of the information coming into each eye goes to each of the hemispheres, with the result, as you point out, that if you are looking forward, things you see to the right of where you are looking are being picked up initially by your left hemisphere and things to the left by your right hemisphere.

To look at hemispheric differences, we ask our participants, who are usually either college students or retired adults, to look at the center of the screen. We then display words (or pictures, or other types of stimuli) fairly rapidly - so people can't move their eyes fast enough to fixate them directly - to the left or the right side of a computer screen. By comparing how people respond (for example, whether they can accurately remember a word) when it was processed first by the left hemisphere versus by the right hemisphere, we can test ideas about what each hemisphere is capable of and whether one hemisphere has better, or different, abilities compared to the other.

Often, we also measure brain electrical activity in these experiments because that provides rich information about how processing is unfolding over time: we can track what happens as the eyes send information to visual processing areas in the brain, as people pay attention to a word, access its meaning from memory, and add this new information into their unfolding understanding of a sentence, and as people, in some cases, decide how to respond and then prepare to press a button to register their response. With electrophysiological measures we can thus find out not only THAT the two hemispheres do something different but WHEN and HOW.

In general, the kinds of hemispheric differences that were uncovered in split-brain patients have been replicated (and then extended) using these techniques in people with intact brains. This sometimes surprises people, including my fellow cognitive neuroscientists. The idea that the two hemispheres perceive things differently, attach different significance to things, obtain different meanings from stimuli, and, sometimes, make different decisions about what to do seems like it should be an exotic side effect of the split-brain condition. When the hemispheres are connected, don't they just share all the information and operate in a unified fashion?

The answer is, no, they don't.

They don't, in part, because they can't. Processing within each hemisphere relies on a rich, dense network of connections. The corpus callosum that connects the hemispheres is big for a fiber tract, but it is tiny compared to the network of connections within each hemisphere. Physically, then, it doesn't seem feasible for the hemispheres to fully share information or to operate in a fully unified fashion. Moreover, in a lot of cases, keeping things separate is (literally!) the smarter way for the hemispheres to function. Dividing up tasks and allowing the hemispheres to work semi-independently and take different approaches to the same problem seems to be a good strategy for the brain ... just as it often is in a partnerships between people.

It makes sense to have specialized brain regions, just as it makes sense to have divisions of labor in other areas of life. But why have specialized hemispheres? In other words, do you think there's something general that can be said about the sorts of processing that occur in the left hemisphere versus the right hemisphere, or is each simply a constellation of somewhat distinct, specialized regions?

Specifically how and why the hemispheres differ remains a mystery. They are actually remarkably similar physically, and this is one reason I think that studying hemispheric differences is critical for the field.

Over the past decade or so, a lot of effort has been put into "mapping" the human brain - that is, linking areas that differ anatomically (have different inputs, outputs, types or arrangements of neurons, and/or neuropharmacology) to different functions. From this, we hope we can learn something about how and why these anatomical differences matter. However, in doing this, the field has also uncovered a lot of hemispheric asymmetries - cases in which, for example, a left hemisphere brain area becomes active and its right hemisphere homologue (with the SAME basic inputs, outputs, etc.) is much less active (or vice versa). This should really surprise us: here are two brain areas that are essentially the same on all the dimensions the field is used to thinking about, yet they behave strikingly differently. There must be physical differences between them, of course - but then, this means that those "subtle" differences are much more critical for function than the field has appreciated.

My own view is that studies of hemispheric differences will help to move the field away from thinking in terms of mapping functions onto localized brain areas. I believe that cognitive functions arise from dynamically configured neural networks. On this view, the role played by any given brain area is different depending on the state of the network of which it is currently a part, and how activity unfolds over time often matters more than where it is in the brain.

Why do the hemispheres differ? I think it is because even small differences in something like the strength with which areas are connected can lead to very different dynamic patterns of activation over time - and thus different functions. For language comprehension in particular, my work has shown that left hemisphere processing is more influenced by what are sometimes called "top-down" connections, which means that the left hemisphere is more likely to predict what word might be coming up next and to have its processing affected by that prediction. The right hemisphere, instead, shows more "feedforward" processing: it is less influenced by predictions (which can make its processing less efficient) but then more able to later remember details about the words it encountered. Because of what is likely a difference (possibly small) in the efficacy of particular connections within each hemisphere, the same brain areas in the two interact differently, and this leads to measurable and important asymmetries in how words are perceived, linked to meaning, remembered, and responded to.

This is unlikely to be the only difference between the hemispheres, of course. But I think the answer to your question is that what we see across the pattern of asymmetries is neither a random collection of unrelated differences nor divisions based on one or even a small set of functional principles (e.g., the left hemisphere is "local" and the right hemisphere is "global" ... another popular one). Rather, some of the underlying biology is skewed, and this has far reaching consequences for the kinds of patterns that can be set up over time in the two hemispheres, leading to sets of functional differences that we can hopefully eventually link systematically to these underlying biological causes, and thereby deepen our understanding of how the brain works.

What's surprised you most about the hemispheric asymmetries you've found (or failed to find!) in your own research?

One of my favorite findings came from an experiment in which we used adjectives to change the meaning of the same noun. For example, the word "book" in "green book" refers to something concrete - that is, something for which it is easy to create a mental image. However, given "interesting book" people now usually think about the content of the book rather than its physical form, so the same word has become more "abstract" in meaning.

A lot of research shows that concrete and abstract words are processed differently in the brain. We wanted to see if those differences could be found for exactly the same word depending on what it was referring to, and whether the two hemispheres were similarly affected by concreteness. We found in this experiment, as we had previously in many others, that the left hemisphere is very sensitive to the predictability of word combinations. Fewer nouns can go with "green" than with "interesting," and brain activity elicited in response to "book" reflected this when the words were presented initially to the left hemisphere.

However, to our surprise, it was the right hemisphere that elicited imagery-related brain activity to "green book" compared to "interesting book." Thus, although the left hemisphere is clearly important for language processing, the right hemisphere may play a special role in creating the rich sensory experience that often accompanies language comprehension ... and that makes reading such a pleasure.

Another popular idea is that some people are more "left brained" and others more "right brained." Is there any evidence for individual differences in the extent to which people rely on one hemisphere versus another? More generally, what kinds of individual differences do you see in hemispheric specialization?

There are certainly individual differences in hemispheric specialization across people, but they are very difficult to reliably determine. Where this matters most is in medical contexts: when people are going to have brain surgery (e.g., for epilepsy or tumor resection), physicians would like to make sure that in removing certain brain tissue they are not going to disrupt critical functions like language.

As I mentioned already, most of the time the left hemisphere is more important for speaking, for example, but that isn't true in absolutely everyone. In order to determine if a person's left or right hemisphere is more important for their language production, physicians use things like the WADA test, in which a barbiturate is injected into one hemisphere to temporarily shut it down, allowing the physician to see what each hemisphere can do on its own. This is obviously a very invasive test (and not perfect at that). If it were possible to instead figure out whether someone relied more on their left or right hemisphere by having them look at a spinning figure or answer a few questions, that would obviously be preferable ... but it doesn't work.

There are, of course, differences in how people learn and think, what they like, and what they are like (although, since everyone's brain is different, I think the similarities are actually more surprising than the differences). Some of these differences may arise because of individual differences in how the hemispheres are organized or which hemisphere tends to be used in particular circumstances. Given that the hemispheres do operate somewhat independently, the question of how their independent processing is eventually combined and/or which hemisphere gets to "take control" of processing for a particular task is one that we are only beginning to understand. (In some cases, split-brain patients' hands - one controlled by each hemisphere - literally fought for control of a particular task; it is intriguing to imagine that kind of struggle routinely taking place internally for everyone else!)

However, it seems safe to say that for the most part we all use both sides of our brains almost all the time. We do know a few factors that influence how functions are lateralized and how much they are lateralized. For example, having a "reversed" laterality (with, for example, control of speech in the right rather than the left hemisphere) is more likely for left-handed than right-handed people (although it is important not to overgeneralize from this: the vast majority of left-handed people have the typical lateralization pattern). Moreover, differences have been seen among right-handed people depending on whether or not they have left-handed biological relatives; this is something my lab is beginning to explore. Again, small biological shifts, caused in part by (complex) genetic differences, can lead to different functional patterns, including whether a function tends to be very lateralized or accomplished by both hemispheres.

I will end with one last fact about hemispheric differences that many people may not be aware of, and that is that lateralization of function changes with normal aging. The kinds of lateralized patterns of brain activity I mentioned earlier when talking about brain mapping studies are more common in young adults. Across many types of tasks and many brain areas, these lateralized patterns tend to switch to bilateral patterns in healthy older adults.

Is this because older adults have better learned how to be both logical AND creative? Maybe :-). It is actually difficult to know when this kind of a shift is helpful - for example, bringing extra processing resources to bear on a task to compensate for age-related declines in function - versus when it might be a sign that the brain is simply less good at maintaining a healthy division of labor. Understanding hemispheric specialization is thus also important for discovering ways to help us all maintain better cognitive functioning with age. This is something my laboratory actively investigates, aided by support from the National Institute of Aging as well as the James S. McDonnell Foundation.

Finally, can you recommend any accessible resources for readers who want to learn more about hemispheric asymmetries?

My own interest in hemispheric differences was sparked, in part, by books like Left Brain, Right Brain by Sally Springer and Georg Deutsch and Hemispheric Asymmetry: What's Right and What's Left by Joseph Hellige. These are accessible books written by scientists and well-grounded in the research - although both books are now more than a decade old, so don't reflect current developments in the field. Unfortunately, I don't know of more recent books that are comparably reliable and accessible.

Some readers may be interested to read journal articles on the topic. For example, I drew some of my information about math and the hemispheres from the article, "Arithmetic and the brain" by Stanislas Dehaene, Nicolas Molko, Laurent Cohen and Anna J Wilson in the journal Current Opinion in Neurobiology (2004; Volume 14, pages 218-224). For those interested in language, I (with coauthors Edward Wlotko and Aaron Meyer) have written a fairly accessible review called "What's "right" in language comprehension: ERPs reveal right hemisphere language capabilities" published in Language and Linguistics Compass (2008; Volume 2, pages 1-17).

You can keep up with more of what Tania Lombrozo is thinking on Twitter: @TaniaLombrozo

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The Truth About The Left Brain / Right Brain Relationship

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Is the idea that the left hemisphere of the brain is more logical and the right more intuitive a scientific fact or a cultural fiction? Commentator Tania Lombrozo turns to an expert for answers.

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By the time I'm done changing the optional materials to include these TEDTalks, students are going to think I'm obsessed with TEDTalks...

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There are people who can quickly memorize lists of thousands of numbers, the order of all the cards in a deck (or ten!), and much more. Science writer Joshua Foer describes the technique -- called the memory palace -- and shows off its most remarkable feature: anyone can learn how to use it, including him.

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"A Powerful Way to Improve Learning and Memory" (2016)

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"A Powerful Way to Improve Learning and Memory" (2016)

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Interesting article on retrieval, complete with studies/statistics. My only concern is its analysis of closed-book v. open-book testing - it seems like a more complicated comparison than the article is acknowledging, considering what exactly students are being tested about, why they should care about that material, and the difference between testing to learn and testing for a grade. Some articles seem to be missing the real-life component of these ideas - i.e., students have limited time and often recognize that much of what they learn in any given subject will have NO relevance to what they do with the rest of their lives.

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The movie "Inside Out" (2015) takes us inside the mind of its young protagonist, an 11-year-old girl named Riley, and depicts memory in a way that is sure to resonate with many people. In Riley's mind, her memories are objects - globes colored with emotions - that are stored in a mental space, just as physical objects are stored in a physical space. When Riley experiences an event and creates a new memory, a new globe is produced in her mind, rolling down a ramp like a ball returning in a bowling alley. When Riley re-experiences a past event, a globe is placed in a projector and events are replayed, projected on a screen in her mind. Cognitive psychologists refer to the mental processes involved in the creation of new memories and the recovery of past memories as encoding and retrieval, respectively. The depiction of the mind in "Inside Out" follows centuries of thought on how mind and memory work. Throughout history, scholars have used a common metaphor to talk about memory: The mind is a vast storehouse or space; memories are objects stored in that space; and retrieving a memory is akin to searching for and finding an object in a physical space (Roediger, 1980). To learn something new, according to this view, the challenge lies in getting knowledge "in" one's mental space. Getting it back "out" when needed is important, too, but learning is usually identified with the encoding of new knowledge in memory. Retrieval is assumed to be neutral for learning; retrieval is needed to assess what a person has learned, but retrieval processes themselves are not thought to produce learning. Recent advances in the science of learning and memory have challenged common assumptions about how learning happens. Specifically, recent work has shown that retrieval is critical for robust, durable, long-term learning. Every time a memory is retrieved, that memory becomes more accessible in the future. Retrieval also helps people create coherent and integrated mental representations of complex concepts, the kind of deep learning necessary to solve new problems and draw new inferences. Perhaps most surprisingly, practicing retrieval has been shown to produce more learning than engaging in other effective encoding techniques (Karpicke & Blunt, 2011). This approach, which recognizes the central role of retrieval processes in learning and aims to develop new learning strategies based on retrieval practice, is referred to as retrieval-based learning.
Retrieval Creates Learning:
Research dating back a century has shown that retrieval contributes to learning (for a historical review, see Roediger & Karpicke, 2006a), but the past decade has seen a renewed, intense focus on exploring the benefits of retrieval for learning. This recent research has established that repeated retrieval enhances learning with a wide range of materials, in a variety of settings and contexts, and with learners ranging from preschool ages into later adulthood (Balota, Duchek, Sergent-Marshall & Roediger, 2006; Fritz, Morris, Nolan & Singleton, 2007). A word-learning experiment illustrates some key points about retrieval-based learning. In the experiment (Karpicke & Bauernschmidt, 2011), students learned a list of foreign language words (e.g., Swahili vocabulary words like "mashua - boat") across cycles of study and recall trials. In study trials, the students saw a vocabulary word and its translation on the computer screen, and in recall trials, they saw a vocabulary word and had to recall and type its translation. The students studied a list of vocabulary words, then attempted to retrieve the whole list, studied it again, retrieved it again, and so on across alternating study and retrieval practice blocks. There were several different conditions in the experiment. In one condition, students simply studied the words once, without trying to recall them at all. In a second condition, students continued studying and recalling the words until they had recalled all of them once. After a word was successfully retrieved once, it was "dropped" from further practice - the students did not see it again in the learning session. Other conditions in the experiment examined the effects of repeated retrieval practice. Once a word was recalled, the computer program required the students to practice retrieving the items three more times. One repeated retrieval condition had the three recall trials happen immediately, three times in a row. This condition, referred to as massed retrieval practice, is akin to repeating a new piece of information over and over in your head right after you experience it. Finally, in the last condition highlighted here, the students also practiced retrieving the words three times, but the repeated retrievals were spaced throughout the learning session. For instance, once a student correctly recalled the translation for mashua, the program moved on to other vocabulary words, but prompts to practice retrieval of the translation for mashua would pop up later on in the program. In this way, the retrieval opportunities were spaced throughout the learning session. The key question in this research was, how well would students remember the vocabulary word translations in the long term? Figure 1 shows the proportion of translations that students remembered one week after the initial learning session. Merely studying the words once without ever recalling them produced extremely poor performance (average recall was 1 percent, barely visible on the figure). Practicing until each translation was recalled once was much better. But what about the effects of repeated retrieval practice? Massed retrieval - repeating the translations three times immediately - produced no additional gain in learning. Repeated retrieval enhanced learning only when the repetitions were spaced, and indeed, the effects of repeated spaced retrieval were very large. In a single experiment, simple changes that incorporated spaced retrieval practice took performance from nearly total forgetting to extremely good retention (about 80 percent correct) one week after an initial learning experience (see also Karpicke & Roediger, 2008; Pyc & Rawson, 2010).

Figure 1. Data from Karpicke & Bauernschmidt (2011).

Retrieval Practice is Underappreciated as a Learning Strategy:
If retrieval practice is such a potent learning strategy, one would hope that many learners would practice retrieval to learn many different things in many situations. However, as noted earlier, retrieval is not typically considered an important part of the learning process, and unfortunately, many learners do not practice retrieval as often or as effectively as they could. An emphasis on getting knowledge in memory shows up on surveys of students' learning strategies. In one survey (Karpicke, Butler & Roediger, 2009), college students were asked to list the strategies they use while studying and to rank-order the strategies. The results, shown in Figure 2, indicate that students' most frequent study strategy, by far, is repetitive reading of notes or textbooks. Active retrieval practice lagged far behind repetitive reading and other strategies (for a review of several learning strategies, see Dunlosky, Rawson, Marsh, Nathan & Willingham, 2013). A wealth of research has shown that passive repetitive reading produces little or no benefit for learning (Callender & McDaniel, 2009). Yet not only was repetitive reading the most frequently listed strategy, it was also the strategy most often listed as students' number one choice, by a large margin.

Figure 2. Student study strategy usage. Survey data from Karpicke, Butler, & Roediger (2009).

Why don't learners use repeated retrieval practice more frequently? Many students view retrieval as a "knowledge check"; they test themselves to see if they know something, rather than out of the belief that practicing retrieval itself will help them learn. This means that many students will use a "one-and-done" strategy: If they can recall something once, they believe they have learned it, so they remove it from further practice. Many students study this way when they regulate their own learning (Karpicke, 2009), even though their long-term learning will not benefit from repeated retrieval practice. Instead, a one-and-done strategy will produce long-term performance similar to the recall-once condition in Figure 1. There is some evidence that instructing learners about the benefits of retrieval leads students to report using retrieval practice more frequently when they study on their own (Einstein, Mullet & Harrison, 2012), but the best ways to influence students to practice retrieval remain to be discovered.
Practicing Retrieval Promotes Meaningful Learning
Perhaps another reason retrieval practice is not used more widely is because repeated retrieval may seem like "rote learning." Rote learning - simple memorization based on repetition - is short-lived, poorly organized and does not support the ability to transfer knowledge, make inferences or solve new problems. The outcome of rote learning is obviously not what students and educators aim for. Meaningful learning is essentially the opposite of rote learning: It is long-lasting and durable, coherent and well organized, and supports transfer, inferencing and problem solving. In fact, the past decade of research on retrieval-based learning has firmly established that retrieval practice promotes meaningful learning. Retrieval-based learning may be a more effective means of achieving meaningful learning than other popular active learning strategies. In one example of this (Karpicke & Blunt, 2011), students studied educational texts about science topics using one of two strategies. In a retrieval practice condition, students read a text, then set it aside and spent time recalling and writing down as much as they could remember from it (Roediger & Karpicke, 2006b). They then reread the text and recalled it a second time. In a second condition, students created concept maps while they read the texts. Concept maps are node-and-link diagrams that require learners to think about the relational and organizational structure of materials (Novak, 2013). The students spent the same amount of time studying in the two conditions; the difference was whether they created concept maps or practiced actively retrieving while learning. Figure 3 shows the results of two different final assessments given one week after the learning session. On one assessment, students answered two types of short-answer questions aimed at measuring meaningful learning: verbatim questions, which assessed concepts stated directly in the texts, and inference questions, which required students to make new connections across concepts. On another assessment, the final assessment involved creating a concept map, because concept mapping is often used as an assessment of the coherence and integration of students' knowledge. On the final verbatim and inference questions and on the final concept map assessment, practicing retrieval during learning produced the best performance, even better than studying the material by making concept maps.

Figure 3. Data adapted from Karpicke & Blunt (2011) (from Figure 2, panels A and C).

Several additional studies have established that retrieval practice promotes meaningful learning. Retrieval practice enhances the learning of educationally relevant materials, including educational texts, multimedia presentations, material explained in classroom lectures and a variety of other complex concepts (Jensen, McDaniel, Woodard & Kummer, 2014; Johnson & Mayer, 2009; Larsen, Butler, Lawson & Roediger, 2013; Lyle & Crawford, 2011; Roediger, Agarwal, McDaniel & McDermott, 2011). Retrieval practice also supports students in making inferences, solving new problems and transferring knowledge (Butler, 2010; Chan, 2009; Hinze & Wiley, 2011; McDaniel, Howard & Einstein, 2009; Smith & Karpicke, 2014). Retrieval-based learning is an effective method for improving meaningful learning.
Creating Retrieval-based Learning Activities
Perhaps the best aspect of retrieval-based learning is that it is free. Although there are sophisticated tools that can be used to implement retrieval practice, like classroom clicker systems (Roediger et al., 2011) and other computer-based learning systems (Grimaldi & Karpicke, 2014; Lindsey, Shroyer, Pashler, & Mozer, 2014), retrieval practice does not require special equipment or technology. The essence of retrieval-based learning is taking material you are trying to learn, setting it aside, and spending time actively retrieving the information. Existing educational activities can be converted into retrieval-based learning activities. For instance, answering questions and taking quizzes are effective ways to practice retrieval. In some circumstances, students might answer questions on quizzes or practice worksheets by looking up answers in their notes or books, rather than by attempting to retrieve the answers. One study directly compared this type of open-book questioning to closed-book conditions in which students were required to retrieve the answers to questions rather than looking them up (Agarwal, Karpicke, Kang, Roediger & McDermott, 2008). Answering questions in open-book conditions led to more forgetting over one week than did attempting to retrieve the answers, closed-book and then studying the answers. In other words, closed-book quizzes, which required retrieval practice, were more effective than open-book quizzes, which did not require learners to engage in retrieval. Another study (Blunt & Karpicke, 2014) examined the effectiveness of using concept mapping as a retrieval practice activity. In the experiment, students read texts about science topics and then created concept maps either with or without viewing the texts. In other words, some students created maps while studying the texts whereas other students had to engage in retrieval to create their maps. On a short-answer assessment one week after the learning session, students did better when they had learned by creating concept maps without viewing the texts, as a retrieval practice activity, than by creating maps while studying the texts. Thus, educational activities can be enhanced when they involve retrieval-based learning.
Take Home Points About Retrieval-based Learning
This article has made the case for four take-home points about retrieval-based learning:

Retrieval is a learning event. Practicing retrieval is a simple and effective way to enhance long-term, meaningful learning.

Some effective learning strategies, like retrieval practice, are underutilized. Conversely, the most popular learning strategy among college students - repetitive reading - leads to very limited levels of learning.

When practicing retrieval, retrieve more than once and space your retrievals, rather than massing them all together. Self-testing as a knowledge check is a good idea, but don't stop at just one successful retrieval (one-and-done). Two or three additional spaced retrievals will bolster long-term learning.

Retrieval can happen in a variety of ways, and many existing activities may be converted into retrieval-based learning activities. The key ingredient is to spend time actively retrieving when trying to learn something new.

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A powerful way to improve learning and memory

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The movie "Inside Out" (2015) takes us inside the mind of its young protagonist, an 11-year-old girl named Riley, and depicts memory in a way that is sure to resonate with many people. In Riley's mind, her memories are objects - globes colored with emotions - that are stored in a mental space, just as physical objects are stored in a physical space.

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For when I finally decide to get into podcasts, lol...

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Teaching In Higher Ed Podcast - Teaching in Higher Ed

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This is the space where we explore the art and science of being more effective at facilitating learning. We also share ways to increase our personal productivity, so we can have more peace in our...

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Good selection of strategies, as well as free downloads on helping students build retrieval skills.

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

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

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Retrieval Practice: Unleash the Science of Learning

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

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"Helping Students Memorize: Tips from Cognitive Science" (2019)

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"Helping Students Memorize: Tips from Cognitive Science" (2019)

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Good article for helping students with memorization techniques. While not as relevant in my courses at the moment, this could be a beneficial component of a course design that emphasizes using writing to learn in other classes/learning to write through understanding how to learn (possible purpose of a revised Writing 121 curriculum).

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Faculty Focus | Higher Ed Teaching & Learning

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Helping Students Memorize: Tips from Cognitive Science

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

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"The Surprising Habits of Original Thinkers" (2016)

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"The Surprising Habits of Original Thinkers" (2016)

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Another good TEDTalk to add to my optional materials for Week 1.

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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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"Operation ARA: A computerized learning game that teaches critical thinking and scientific reasoning" (2012)

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"Operation ARA: A computerized learning game that teaches critical thinking and scientific reasoning" (2012)

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While somewhat dated, this idea could be useful in helping my students develop stronger research skills. I would use modern games as examples/tools, though. It might also be a good companion piece to my students' use of "Walk, Talk, Cook, Eat" and "Googlepedia" from Writing Spaces, V. II.

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Operation ARA (Acquiring Research Acumen) is a computerized learning game that teaches critical thinking and scientific reasoning. It is a valuable learning tool that utilizes principles from the science of learning and serious computer games. Students learn the skills of scientific reasoning by engaging in interactive dialogs with avatars. They are tutored by avatars with tutoring sessions that vary depending on how well students have responded to questions about the material they are learning. Students also play a jeopardy-like game against a feisty avatar to identify flaws in research and then generate their own questions to determine the quality of different types of research. The research examples are taken from psychology, biology, and chemistry to help students transfer the thinking skills across domains of knowledge. Early results show encouraging learning gains.

Highlights

► We designed a computerized learning game that teaches critical thinking/scientific reasoning using interactive natural language tutorials. ► The game was designed using the principles from the science of learning, such as active responding, an interesting story line, repeated testing, and engagement with real-life examples and from learning games. ► Early testing shows large learning gains for students at three different types of higher education-an open admissions community college, a state university, and a highly selective private college. ► Student response has been highly positive.

Fitness shoes with rounded soles and an orthopedic appearance promise "more shapely legs and a better butt" in addition to weight loss, reduced cellulite, and improved circulation (University of California Wellness Letter, 2012). Sound too good to be true? Not to consumers who spent $1.1 billion dollars on these shoes in 2010. (They have been on the market since 2000.) The United States Trade Commission investigated these unbelievable claims and because Reebok, one of the manufacturers, could not back up its claim that wearing these shoes would "result in more strength and tone in the buttocks muscles than traditional walking shoes," Reebok agreed to settle the case with the Trade Commission and refund $25 million to consumers (ElBoghdady, 2012).

Bogus claims about toning shoes may seem like a frivolous concern, but this example is one of countless similar scams that represents massive public disregard for the value of assessing claims, seeking evidence, and understanding scientific thinking. Millions of dollars are spent annually on products that are "packaged" as scientific, even though the "research" or evidence is highly suspect or absent. For example, millions of dollars are spent on homeopathic remedies that lack scientific evidence for their effectiveness. Scientific inquiry is important in the workplace, especially as jobs become increasingly technical, require specialized skills, and require domain-specific problem-solving, reasoning, and decision making (National Research Council, 2011). Often, the same techniques that are used to sell miraculous products are also used to create or enhance prejudice and hostilities toward other groups and to justify wars and policies that cause harm and sometimes, even death (Lilienfeld, Ammirati, & Landfield, 2009). Ideally, everyone should know how to critically evaluate the products of science and evidence-based claims, even when the topic seems distant from our usual notions of what constitutes science.

We want to educate our students and the general public to be wise consumers of research. We also want them to apply the principles of scientific thinking in their daily interactions and in contexts that bear little resemblance to scientific studies. This is a common goal for critical thinking instruction. To achieve this goal, we conceptualize science as a process, as a way of thinking about and obtaining information that informs the conclusions that we make. For example, if our students were embroiled in an argument over who was more influential to hip-hop, James Brown or Stevie Wonder, we would want them to respond that the argument can be resolved by using what scientists call an operational definition. If someone complains about a colleague who is always late, we hope that they will ask about the sample size (how often was the colleague late?) that supports this conclusion. These are examples in which the principles of scientific thinking are important in daily life.

Of course, we hope that we are also educating future scientists, helping them to "think like a scientist." Scientific thinking is needed for everyone-future scientists and future consumers of science-because we are all exposed to causal claims made by scientists and nonscientists on a daily basis. These claims are made on the Internet, television, news reports, advertisements, and the wide range of social media. Does wine really lower the risk of heart attacks? Are the screening devices at airports really safe? Can I teach my toddler to read? Will copper bracelets cure arthritis? The list of claims is endless. Operation ARA (Acquiring Research Acumen) was designed to teach students how to determine what and whom to believe.

We took up the challenge of creating the best tools for learning by applying what we know about the science of learning and adapting those principles for a generation of "digital natives," a term that describes today's young adults who have never known a world without computers and the internet. They are more likely to get their information from the vast array of nonprint media available to them than from textbooks. According to a report assembled by Frontline (Public Broadcast System, 2010), digital natives aged 13-17 average 1742 text messages a month, 91% use their profiles on social media websites to stay in touch with friends, and they spend an average of 4.5 h a day viewing screen media (internet, television, etc.), excluding games. Not surprisingly, less time is spent learning from books, and more of today's students are finding it difficult to learn from printed text. We can lament the loss of textbook skills and design educational interventions that help students achieve better learning from books, and we can also provide high quality learning experiences based on how students actually learn, including computerized learning games. There are benefits to learning from computerized games, which include improvements in visual attention and faster response times (Dye, Green, & Bavelier, 2009).

We developed a computerized learning game, Operation ARA that incorporates the best findings from the science of learning. Mayer (2008) reminded educators that no matter how advanced the technology is for educational content, if the fundamental principles of learning are not incorporated into the design of the materials, then quality learning is unlikely to occur. Here is a sampling of some of the learning principles we used in developing this program (Halpern & Hakel, 2003).

Meaningful learning of complex material is NOT a spectator sport. Deep learning requires that the learner be actively engaged with the material. Operation ARA requires that learners demonstrate their learning consistently throughout the program. There are three modules in Operation ARA: Basic Training in which students use an interactive e-book to learn basic principles of scientific thinking, such as the need for control groups and the difference between correlational and causal research designs; Proving Ground where students play a jeopardy-like game against an avatar who assumes the role of a competing student; and Active Duty in which students ask questions in order to determine whether a reported study is reliable or flawed. Early in the program, students respond to questions, but by the end of the program they are asking questions. Unlike a standard book, students cannot merely move their eyes across text or wipe a highlighter across a page. They cannot move through the program without demonstrating their learning at each step.

Recent research has shown that repeated testing, particularly when the test requires the generation of information, enhances learning. This practice produces gains in understanding and memory when the tests are aligned with important content (Karpicke and Roediger, 2007, Roediger et al., 2011). According to standard "memory trace" theories of how people remember, the act of remembering strengthens some memory traces and weakens, or perhaps fails to strengthen, others, a fact that should influence how we test students. There are complex mathematical models and functions that describe the course of remembering (or forgetting) over time (Anderson et al., 2004, Oberauer and Lewandowsky, 2011). When students practice retrieval, the strength of the memory trace is increased and the shape of the forgetting curve is altered to reflect the fact that the just-retrieved information is now more likely to be retrieved (i.e., less likely to be forgotten) in the future. In more common language, this principle is sometimes called the "testing effect," which refers to the finding that by responding to a question, the learner is practicing retrieval, thus making later retrieval of the same material more likely to be successful. As students progress through Operation ARA their understanding of the concepts and principles of science are repeatedly tested. In Basic Training, students answer a number of multiple choice questions at the end of each chapter and then participate in trialog discussions with avatars where the understanding of the material from the chapter is clarified and reinforced. Students evaluate 11 cases in the Proving Ground, and for each case they must apply their understanding of scientific concepts to determine whether the research is reliable or flawed. In Active Duty students generate their own questions about abbreviated research descriptions in order to determine whether the research is flawed. In this module, students actively recall the scientific concepts in order to generate questions, with little scaffolding at this point in the game.

Learning and retention occur over time, so it is not surprising that much of the research literature on these topics is concerned with time intervals-the time spent on the initial learning, the time between acquisition (initial learning) and recall, and the length of the time intervals between recall trials. One of the primary "laws" of learning is that spaced practice is generally superior to massed practice. Spaced schedules of studying and testing produce better long-term retention than single or few study sessions or tests (Karpicke & Bauernschmidt, 2011). Students work through Operation ARA over time and repeatedly practice and apply concepts in different contexts.

As students move through the interactive chapters during Basic Training, they receive computer-generated tutoring that varies depending on how well the student responds. The mixed-initiative dialogue used for tutoring is similar to Auto Tutor, which was designed by Art Graesser and numerous colleagues. Students using Auto Tutor have shown considerable learning gains comparable to one-on-one human tutoring (Graesser et al., 2004, VanLehn et al., 2007). The type of tutoring that students receive following each chapter is determined by the number of questions they answer correctly about the chapter they just read. Effective tutors need to both correctly gauge and adapt to the student's current level of understanding. A successful adaptive tutor chooses problems that specifically address the level of the student's prior knowledge and take previous test scores into consideration (Graesser, Conley, & Olney, 2012).

In compliance with these criteria, students are adaptively placed in one of three tutoring conditions based on their current level of knowledge, which is gauged by their scores on the previous multiple choice tests. If the human students demonstrate a low-level understanding of the concept, they receive the vicarious learning trialog in which they observe a virtual teacher tutoring a virtual student, with limited active participation. Vicarious learning conditions have shown significant learning gains specifically for low prior-knowledge students (Driscoll et al., 2003). In order to maintain engagement during vicarious learning, the students are asked to respond to questions about the tutoring situation. For example, the virtual teacher might ask the human student whether the virtual student understands the concept or whether the virtual student's answer was correct. If the learners demonstrate a moderate understanding of the concept, they receive the standard tutoring trialog, and they are tutored by the virtual teacher. For example, the virtual teacher might ask the human student to provide a definition of the concept and scaffold the student using hints, prompts, feedback, and misconception correction. If the learners demonstrate good understanding of the concept, they interact with the teachable agent trialog, and the human student tutors a virtual student. For example, virtual students might tell the human student that they do not really understand the concept and offer an incorrect explanation. The human student would then have to explain to the virtual students what the concept is and why they were incorrect.

Standard learning texts always emphasize the need for feedback or knowledge of results as an essential component for enhancing learning, but more recent reviews have shown that feedback is a complex variable that sometimes hinders learning and sometimes enhances it (Kluger and DeNisi, 1996, Kluger and DeNisi, 1998, van der Kleij et al., 2012). When data from many studies were combined in a meta-analysis, the overall effect size for feedback was a healthy d = .41, but this sizable effect masks the fact that over 1/3 of the studies showed that feedback had negative effects on learning and performance. Feedback is important in that it provides information to the learner about his or her own performance, but the learner still has to derive meaning from it and it may be that the way learners interpret feedback is what determines when it will be beneficial. What does feedback really mean to a learner? Is successful learning indicative of the intelligence or skill of the learner, the difficulty of the learning task, the way learning performance is assessed, or the amount of effort expended? Learning settings need to be designed so that the type of feedback is matched to the intended reason for the learning task. Even a formerly simple concept like feedback needs to be understood in terms of its intended use so that learners do not interpret feedback as punishment, which leads to resistance to the information provided by the feedback.

Most research reviews conclude that feedback should be given at appropriate intervals, probably with increasing length of the intervals as learning progresses (Winstein & Schmidt, 1990). Thus, early in the learning process, more frequent feedback is needed than later in the learning process. In this way, learners can become judges of their own performance and rely less upon external knowledge of results than they would with more constant feedback. We used this principle in the design of Operation ARA. As learners progress through the program, they receive feedback that is increasingly less frequent and less detailed.

Variability during learning has been shown to be important for improving long-term recall and enhancing knowledge transfer. The theme of variability during learning underscores the causal and sometimes contradictory relationship between what happens during learning and long-term retention. By using examples from different science domains-psychology, biology, and chemistry, students need to concentrate on the underlying scientific principles, which actually makes the learning more difficult. Learners need to expend more effort when learning conditions are variable, but the hard work of effortful learning pays large dividends when long-term retention is assessed.

Like practice at retrieval, varied learning conditions pay high dividends for the effort exerted. In the jargon of cognitive psychology, when learning occurs under varied conditions, key ideas have "multiple retrieval cues" and thus are more "available" in memory. For example, educational research suggests that significant learning gains can occur when different types of problems and solutions are mixed in the same lesson, even though the initial learning can take significantly longer (Perry, Samuelson, Malloy, & Schiffer, 2010).

In order to learn well, students need to be motivated and engaged in the process. According to a report from the Bureau for Labor Statistics (2011), time spent playing video games varies greatly with age. On an average weekend day, 15-19-year-old Americans read an average of 6 min and play video games 1 h and 6 min. At the time of this writing, the game Angry Birds has been downloaded tens of millions of times. There are numerous international gaming competitions with entries from most countries in the world. Thus, if we could harness only a small percentage of the energy and enthusiasm spent on computerized games so that game playing resulted in learning, we would have made great strides in creating quality learning environments. Here are some of the properties of the best selling games that we applied to Operation ARA.

Just as Operation ARA utilizes the best of what we know about how people learn, it also borrows from the growing literature on serious games. In Operation ARA learning is embedded in an intriguing story line. The game begins when players join the fictitious Federal Bureau of Science where they learn about an evil extraterrestrial plot. Apparently, the extraterrestrials are publishing poorly designed research in a variety of media outlets with the goal of confusing humans as to what constitutes good science. However, the aliens are disguised as human beings. Therefore, the player is given the task of learning the scientific method so that he can help the FBS identify the extraterrestrials. Along the way, an avatar protagonist learns that his brother is being held captive by Fuaths from the planet Thoth and apparently has broken under the stress. To complicate matters, our protagonist finds that he is falling in love with a Fuath, thus blending traditional action and romance scripts. But, all is not what it seems. We later learn that the extraterrestrials are stealing our natural resources. The plot has surprising twists and ultimately it is up to the learner to save the world by identifying the aliens among us. There are double-agents, humor, romance, political intrigue, and more all taking place within a "green" theme. Additional descriptions about Operation ARA (formerly known as Operation ARIES!) are available in Butler, Forsythe, Halpern, Graesser and Millis (2010) and Millis et al. (2011).

As students progress through the program, they receive incentives for responding correctly, which include earning points in a competition against a feisty and somewhat irritating student avatar. Later in the game, the learner has to decide if particular research is reliable or flawed, and if they are correct, human researchers are set free and alien researchers are sent to jail. Errors can be disastrous because they would allow dangerous aliens to be free and innocent humans would go to jail. Ultimately, good decisions will save the world, which we believe to be a motivating outcome.

There are yet other psychological principles that are built into the game to optimize motivation and engagement. These include giving the student control through choices, presenting problems that are at the optimal zone of challenge (not too easy and not too difficult), high amounts of interactivity between the game and the student, feedback on their contributions, and engaging media. All of the design decisions in developing ARA were motivated by scientific principles to enhance cognition or motivation.

Of course, we hope that readers are looking for evidence that students actually learn the skills of scientific thinking by playing Operation ARA. Although we have been working on this learning game for several years, data collection is still in its early stage, with initial results looking positive. This paper presents some early findings. Results from several other preliminary studies of the effectiveness of Operation ARA are available from the first author upon request. As the number of students with different sorts of backgrounds who use Operation ARA increases, we expect to have many more studies completed over the next several years.

View full text

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Operation ARA: A computerized learning game that teaches critical thinking and scientific reasoning

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Operation ARA (Acquiring Research Acumen) is a computerized learning game that teaches critical thinking and scientific reasoning. It is a valuable le...

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"Problem Library" (University of Delaware IFTUE)

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"Problem Library" (University of Delaware IFTUE)

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While the writing problems aren't anything new, and the English literature ones seem dated, there are some neat problems for other disciplines, especially the sciences, that I could use to show my students how critical thinking (and activities/assignments) vary between disciplines/majors/courses.

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Problem Library: 116

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

Watson, George

Physics and Astronomy

1 Hour or Less

Introductory (non-majors)

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

Tallitsch, Robert

Biological Sciences

1 Hour or Less

Intermediate (majors and non-majors)

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

Duch, Barbara

Physics and Astronomy

3-6 hours

Introductory (non-majors)

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

Groh, Susan

Chemistry

1-3 Hours

Introductory (majors and non-majors)

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

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So cool! Show to students!

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Fun test related to attention. Could be a neat exercise for students after course revisions.

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"Processes - Building Memory Power" (2016)

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"Processes - Building Memory Power" (2016)

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It's part 2 of the previously saved video. Similar benefits!

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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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Quick introduction to the different components of memory - good for students who don't have time to read more complex articles. Also, it dispels the idea of "muscle memory", replacing it with procedural memory.

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Although still an article, this text offers excellent activities to use to demonstrate why attention matters, how it can be affected, and what this means for students.

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Inside Higher Ed | Higher Education News, Events and Jobs

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Attention is how the mind prioritizes. The brain's attention circuits stay busy throughout our waking hours, directing on a millisecond-by-millisecond basis where our limited cognitive resources are going to go, monitoring the information that floods into our senses, and shaping conscious experience. Attention is one of the most mysterious and compelling topics in cognitive science. Years of research on the subject are now paying off handsomely in the form of recent advances in our understanding of how these mechanisms work, on both theoretical and physiological levels. And the more we learn, the more we realize that these findings aren't just important for theory-building -- they offer myriad practical applications that can help people function more effectively across all aspects of life. Teaching and learning is one area where attention research is especially useful for helping us get better at what we do.

In my book Minds Online: Teaching Effectively with Technology, I foreground attention as the starting point for everything designers of college-level online learning experiences should know about human cognition. Without attention, much of what we want students to accomplish -- taking in new information, making new connections, acquiring and practicing new skills -- simply doesn't happen. And thus, gaining students' focus is a necessary first step in any well-designed learning activity, whether online or face-to-face.

But how does this principle play out in a contemporary learning environment littered with tempting distractions -- the smartphones that accompany students to class, social networks that let us reach out to friends around the clock, the sites for games, media, and shopping that beckon every time we open our browsers? It's especially concerning given how overly optimistic people tend to be about their ability to juggle different tasks. As psychologists Christopher Chabris and Daniel Simon eloquently explain in The Invisible Gorilla: And Other Ways Our Intuitions Deceive Us, human beings are notoriously bad at knowing what we can handle, attention-wise. Essentially, we lie to ourselves about what we notice and what we know, believing that we take in much more that we actually do.

For our students, this adds up to a serious drain on learning. And as learning environments become more complex, it is a drain they can't afford. Consider, for example, some forms of blended learning in which students master foundational knowledge outside of class, usually through online work, then spend class time on focused application and interaction with instructors and classmates. A tightly scheduled and synchronized system like this can work beautifully, but doesn't allow much margin of error for wasted time and scattered focus.

So what can we do about this situation? One strategy is to educate students about the limits of attention and just how much they miss when they choose to multitask. This, however, is easier said than done. Incorporating a learning module on attention is straightforward enough, but what would it take for such a module to be effective? First, it would need to be brief and to the point, reinforcing just a few crucial take-home messages without a great deal of history, theory or other background more appropriate to a full-length course in cognition. At the same time, quality control would be a major concern, especially for the module to be usable by an instructor without academic training in cognitive psychology. Just Googling for materials on attention brings up at least as much pseudoscience as reputable work, and without this solid scientific grounding, a module on the dangers of multitasking could easily devolve into a "Reefer Madness"-style experience, more laughable than persuasive.

Keeping these caveats firmly in mind, I've worked with my instructional designer colleague John Doherty to create a free-standing, one-shot online learning module called Attention Matters that instructors can drop into existing courses as an extra credit or required assignment. Besides being scrupulous about the science, John and I prioritized interactivity and use of the multimedia capabilities of online learning -- enabling us to show students, not just tell them, what distraction can do to performance in different contexts. Too many online learning activities consist essentially of glorified PowerPoint slides, so although there is a certain amount of text within our module, we put most of the emphasis on media, demonstrations, self-assessment and discussion.

As an example, we used a demonstration we called the "Selective Reading Challenge" to show students how attention mechanisms constantly filter incoming information, and also, how little we remember of information we don't attend to. The demonstration consists of a page of text, alternating lines of bold and regular typefaces. Students are instructed to pay attention to only the bold lines, ignoring everything else, then proceed immediately to the next page. In the "to be ignored" text, we hide a few stimuli that may break through to awareness -- a couple of common names (Michael, Emily, Stephen, Christina), that if they belong to you, will probably pop out, as well as a few attention-grabbing emotional terms (911, murder). After completing the "selective reading," students are invited to go back review the entire page of text -- bold and regular -- to see what they missed, and what they (likely) don't remember at all even though it was well within the field of vision.

Other demonstrations illustrate the dramatic slowdown in processing that takes place when we multitask among competing activities. We present an online version of the classic "Stroop effect" to illustrate how distraction -- even from other mental processes going on at the same time -- can make a simple activity slow and inaccurate. The task involves naming the colors of a sequence of multicolored words -- not a difficult task, except when the words are themselves color names. red, green, blue, and so on - that contradict the colors they are printed in. Lastly, we pulled in several video clips from around the Web to drive home the multitasking point. One shows a prank "driving test" in which unsuspecting students were told to text while navigating a practice course, with predictably disastrous results. Another classic clip called "The Amazing Color Changing Card Trick," created by psychologist Richard Wiseman, dramatically illustrates how attending to one part of a scene causes us to miss major developments going on in practically the same location.

These videos, activities and demonstrations form the anchor for brief, impactful student learning activities throughout the module. Students respond to discussion prompts asking them whether the demonstrations worked on them as predicted, and what they may mean for everyday attention. They also complete self-quizzes with feedback that target the different learning outcomes for each part of the module. At the end, they revisit what they have learned in a brief self-reflection and survey on attitudes and beliefs about attention and its importance for learning.

Attention Matters is an exciting project, offering us the opportunity to apply cognitive science in a novel and - we hope - useful way. The project also has a research component, through which we will be gathering data on student attitudes and beliefs about their own attentional capabilities, as well as on the frequency of different multitasking behaviors in their own lives.

There's another important side to Attention Matters, and that has to do with the collaboration between an instructional design expert and a Subject Matter Expert, or SME. Much has been written about the virtues of instructional design experts' pairing up with SMEs, and yet, such collaborations remain fairly rare within higher education. We hope that this project demonstrates the real benefits to be gained - perhaps motivating others to take the plunge.

It's still too early to know what the long-term impacts of Attention Matters are going to be, or to predict exactly what we might discover about student attitudes and behaviors around multitasking. But I do foresee that as seismic change continues to occur in higher education, we will see more educators entering similar new territory - collaboratively creating focused, technologically delivered learning modules that live outside of traditional courses and use learning theory and cognitive science as the basis for design. And in our case, we may be able to add to our arsenal of strategies for getting students to become better stewards of their own attention.

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Essay calls for professors to start teaching students about distraction and attention

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It's time for professors to move beyond complaining about how students are distracted by social media, writes Michelle Miller. Faculty members need to teach about why attention matters.

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"Can millennials pay attention to classwork while texting, tweeting and being on Facebook?" (2015)

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"Can millennials pay attention to classwork while texting, tweeting and being on Facebook?" (2015)

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Good short article on how attention and memory are impacted by digital media. Dispels some myths and acknowledges that the intersections of memory, attention, and media are complex. This could be a good resource for students to explore, especially those who think they're masters of "digital multitasking".

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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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Good summary of everything related to the neuroscience of learning. I've used a similar TEDTalk as part of my instructional materials, but this one was more recently produced.

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The neuroscience of learning. | Siddharth Warrier | TEDxSIESGST

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"How to practice effectively...for just about anything - Annie Bosler and Don Greene" (2017)

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"How to practice effectively...for just about anything - Annie Bosler and Don Greene" (2017)

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Another excellent resource for students, especially when I'm encouraging them to practice their writing. It shows how that practice affects their brains. Also has really effective visuals!

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

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"Neurons and What They Do - An Animated Guide" (2013)

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"Neurons and What They Do - An Animated Guide" (2013)

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Quick and easy guide to understanding how neurons work - great resource for students!

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Neurons and What They Do ~ An Animated Guide

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"Learning and memory under stress: Implications for the classroom" (2016)

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"Learning and memory under stress: Implications for the classroom" (2016)

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Excellent images and an exploration of how time plays into the impacts of stress on memory quality/quantity - " stress affects memory in a time-dependent manner, often enhancing memory formation around the time of the stressful encounter but impairing memory retrieval and the acquisition of information encoded long after the stressful event. These effects depend on interactions between NA and cortisol in the amygdala and are thus often stronger for emotional than for neutral learning material." Also this - "Stress does not only induce a deficit in memory retrieval and memory updating, it also changes the way information is stored and retrieved by multiple memory systems. Stress before learning may bias students towards rigid forms of learning, which may hinder the successful transfer of knowledge and reduce cognitive flexibility in problem solving"

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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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"After watching this, your brain will not be the same" (2015)

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"After watching this, your brain will not be the same" (2015)

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Good companion piece to shorter video on neuroplasticity - could be used as the optional viewing on neuroplasticity, while the other is the required.

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

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Excellent short video guide to neuroplasticity - could be used in teaching without overwhelming students with new information not directly relevant to reading/writing/research

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Good link for easy access to UDL guidelines

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

Reenvisioning the UDL Guidelines

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

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

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

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"Neuroplasticity, Optimal Learning & Regulations" (2022)

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"Neuroplasticity, Optimal Learning & Regulations" (2022)

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First half covers similar topics to QM workshop on EYCD; sections on stress and sleep debt could be used as optional reading materials for Writing 121/122 depending on how curricula are revised

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"Growing Brains, Nurturing Minds—Neuroscience as an Educational Tool to Support Students’ Development as Life-Long Learners" (2022)

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"Growing Brains, Nurturing Minds—Neuroscience as an Educational Tool to Support Students’ Development as Life-Long Learners" (2022)

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Zone of proximal development; acknowledging how body/mind interact when it comes to learning (already doing this); "a surviving brain is not a learning brain"; motivation; creating spaces of belonging through empathy and inclusivity; formative over summative assessment

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

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

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

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

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

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

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

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

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

1.1. Learning and Neuroplasticity

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

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

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

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

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

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

1.1.1. Using Neuroplasticity as Educational Content

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

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

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

1.1.2. Using Neuroplasticity to Guide Learning Design

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

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

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

1.2. Learning Motivation and Reward

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

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

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

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

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

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

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

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

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

1.2.2. Using Neuroscience to Guide Learning Design and Intrinsic Motivation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2. Discussion

2.1. Teaching Students about Their Developing Brains

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

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

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

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

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

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

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

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

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

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

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

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

3. Conclusions

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

Funding

This research received no external funding.

Institutional Review Board Statement

The study did not require ethical approval.

Informed Consent Statement

Data Availability Statement

The study did not report any data.

Conflicts of Interest

The authors declare no conflict of interest.

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

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

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

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So cool!

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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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"4 trends that will shape the future of higher education" (2022)

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"4 trends that will shape the future of higher education" (2022)

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Emphasis on active learning, though somewhat disconcerting that the article seems to stress that education should always support capitalistic endeavors

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Education

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

Senior Director, Strategic Communications and Thought Leadership, Minerva Project

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4 trends that will shape the future of higher education

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