What does The Science of Learning report teach us?

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A while ago I wrote an article called The Science of Learning which was, in turn, based on a keynote speech I’d given which distilled the principles of cognitive science into six key points.  For each of the six points, I summarised the cognitive principle at work and explored the practical implications for the classroom.

You can download the slides from my keynote here.  You can read my article on the practical application of cognitive science here and my article on the science of learning here.

Today, a new non-profit organisation in the US called Deans for Impact produced a report, endorsed by the cognitive scientist Dan Willingham, of the same name which did much the same thing… you’d think I’d be jealous of all the attention it’s getting but I’m not because it happens to be really rather good.  In effect, it summarises a lot of what I’ve also written about in recent years (over the course of several thousand words!) in just a few pages…and does so with greater rigour! My advice, therefore, is to ignore me and read the report…

In just ten pages, the report summarises a broad range of research into thirteen cognitive principles and, for each principle, lists implications for classroom teaching.

You can download the report here: The_Science_of_Learning.

Science of Learning

Find out more about the authors of the report at http://www.deansforimpact.org

Below, I recount each of the thirteen cognitive principles – followed by  an abridged list of classroom implications – and, where relevant, provide links to articles I’ve written that expand on those principles.  At the end I include the full list of references from the Deans for Impact publication and highlight the papers and books I would particularly recommend.

Cognitive principle 1: Students learn new ideas by reference to ideas they already know.

Practical implications:

  • A well-sequenced curriculum is important to ensure that students have the prior knowledge they need to master new ideas.
  • Teachers use analogies because they map a new idea onto one that students already know.

Further reading:

My article on using cognitive principles to inform your lesson planning can be found here

Here’s my article on teaching knowledge before skill

Find out about using  metaphor to make ideas stick in my piece on outstanding teaching

Cognitive principle 2: To learn, students must transfer information from working memory (where it is consciously processed) to long-term memory (where it can be stored and later retrieved). Students have limited working memory capacities that can be overwhelmed by tasks that are cognitively too demanding. Understanding new ideas can be impeded if students are confronted with too much information at once.

Practical implications:

  • Teachers can use “worked examples” as one method of reducing students’ cognitive burdens.
  • Teachers can use multiple modalities to convey an idea; for example, they can speak while showing a graphic. If teachers take care to ensure that the two types of information complement one another — such as showing an animation while describing it aloud — learning is enhanced.
  • Making content explicit through carefully paced explanation, modeling, and examples can help ensure that students are not overwhelmed.

Further reading:

My article on the zone of proximal development

My article on attention, encoding, storage and retrieval

Cognitive principle 3: Cognitive development does not progress through a fixed sequence of age-related stages. The mastery of new concepts happens in fits and starts.

Practical implications:

  • Content should not be kept from students because it is “developmentally inappropriate.” To answer the question “is the student ready?” it’s best to consider “has the student mastered the prerequisites?”

Further reading:

My article on expertise

Cognitive principle 4: Information is often withdrawn from memory just as it went in. We usually want students to remember what information means and why it is important, so they should think about meaning when they encounter to-be-remembered material.

Practical implications:

  • Teachers can assign students tasks that require explanation (e.g., answering questions about how or why something happened) or that require students to meaningfully organize material.
  • Teachers can help students learn to impose meaning on hard-to-remember content. Stories and mnemonics are particularly effective at helping students do this.

Further reading:

My article on forgetting

Cognitive principle 5: Practice is essential to learning new facts, but not all practice is equivalent.

Practical implications:

  • Teachers can space practice over time, with content being reviewed across weeks or months, to help students remember that content over the longterm.
  • Teachers can explain to students that trying to remember something makes memory more long-lasting than other forms of studying. Teachers can use low- or no-stakes quizzes in class to do this, and students can use self-tests.
  • Teachers can interleave (i.e., alternate) practice of different types of content.

Further reading:

My article on deliberate practice

My article on an ethic of excellence

Cognitive principle 6: Each subject area has some set of facts that, if committed to long-term memory, aids problem-solving by freeing working memory resources and illuminating contexts in which existing knowledge and skills can be applied. The size and content of this set varies by subject matter.

Practical implications:

  • Teachers will need to teach different sets of facts at different ages. For example, the most obvious (and most thoroughly studied) sets of facts are math facts and letter-sound pairings in early elementary grades.

Further reading:

My article on teaching knowledge

My article on fast mapping

Cognitive principle 7: Effective feedback is often essential to acquiring new knowledge and skills.

Practical implications:

  • Good feedback is:
    • Specific and clear;
    • Focused on the task rather than the student; and
    • Explanatory and focused on improvement rather than merely verifying performance.

Further reading:

My article on effective feedback

Cognitive principle 8: The transfer of knowledge or skills to a novel problem requires both knowledge of the problem’s context and a deep understanding of the problem’s underlying structure.

Practical implications:

  • Teachers can ensure that students have sufficient background knowledge to appreciate the context of a problem.

Further reading:

My articles on great teaching

My article on high expectations

Cognitive principle 9: We understand new ideas via examples, but it’s often hard to see the unifying underlying concepts in different examples.

Practical implications:

  • Teachers can have students compare problems with different surface structures that share the same underlying structure. For example, a student may learn to calculate the area of a rectangle via an example of word problem using a table top.
  • For multi-step procedures, teachers can encourage students to identify and label the substeps required for solving a problem.
  • Teachers can alternate concrete examples (e.g., word problems) and abstract representations (e.g., mathematical formulas) to help students recognize the underlying structure of problems.

Further reading:

My article on constructive alignment and the SOLO taxonomy 

My articles on transfer parts one and two

Cognitive principle 10: Beliefs about intelligence are important predictors of student behavior in school.

Practical implications:

  • Teachers should know that students are more motivated if they believe that intelligence and ability can be improved through hard work.
  • Teachers can contribute to students’ beliefs about their ability to improve their intelligence by praising productive student effort and strategies (and other processes under student control) rather than their ability.
  • Teachers can prompt students to feel more in control of their learning by encouraging them to set learning goals (i.e., goals for improvement) rather than performance goals (i.e., goals for competence or approval).

Further reading:

My article on developing a growth mindset in the classroom

Carol Dweck on mindset and on the power of ‘yet’

My article on behaviour

Cognitive principle 11: Self-determined motivation (a consequence of values or pure interest) leads to better longterm outcomes than controlled motivation (a consequence of reward/punishment or perceptions of self-worth).

Practical implications:

  • Teachers control a number of factors related to reward or praise that influence student motivation, such as:
    • whether a task is one the student is already motivated to perform;
    • whether a reward offered for a task is verbal or tangible;
    • whether a reward offered for a task is expected or unexpected;
    • whether praise is offered for effort, completion, or quality of performance; and
    • whether praise or a reward occurs immediately or after a delay.

Further reading:

My article on the growth mindset

My article on nature versus nurture

Cognitive principle 12: The ability to monitor their own thinking can help students identify what they do and do not know, but people are often unable to accurately judge their own learning and understanding.

Practical implications:

  • Teachers can engage students in tasks that will allow them to reliably monitor their own learning (e.g., testing, self-testing, and explanation).

Further reading:

My article on high expectations

My article on excellence

My article on the Pygmalion Effect 

Cognitive principle 13: Students will be more motivated and successful in academic environments when they believe that they belong and are accepted in those environments.

Practical implications:

  • Teachers can reassure students that doubts about belonging are common and will diminish over time.
  • Teachers can encourage students to see critical feedback as a sign of others’ beliefs that they are able to meet high standards.

Further reading:

My article on academic behaviours

My article on the foundations of great teaching

More recommended reading:

The importance of questioning – here and here

My article on memory being the residue of thought – here

*

The Deans for Impact report ends with some words of warning – that teachers should be able to recognise common misconceptions of cognitive science that relate to teaching and learning.  And it goes on to share some of these myths that surround cognitive science, including:

  • Students do not have different “learning styles.”
  • Humans do not use only 10% of their brains.
  • People are not preferentially “right-brained” or “leftbrained” in the use of their brains.
  • Novices and experts cannot think in all the same ways.
  • Cognitive development does not progress via a fixed progression of age-related stages.

I’m glad the report adds this final caveat because I am particularly concerned about the way cognitive science is being applied to the classroom and have written a two-part article for SecEd magazine (which will be published in November – read a preview here) warning of the dangers of teachers hijacking neuroscience when there is, as yet, no evidence that neuroscience can be meaningfully applied to the classroom.

Here’s a short extract from that article to give you an indication of my point of view:

Neuroscience has gained traction in recent years and people have become more interested in learning about how the brain works. This is, of course, a good thing and we should always encourage intellectual curiosity of this kind. It’s partly what makes teaching a profession, after all. The brain is fascinating and, although there remains much mystery about how it works, a lot more is now known that could influence the way we behave and, crucially, the way we teach and learn. But if we insist on using neuroscience to explain common sense approaches to teaching, we are in danger of losing the debate by detracting from the real argument, by making the argument difficult to follow, or by making false connections between behavioural and physical phenomena.

Among the concerns I raise in the article, I say I think neuroscience language is being used as a surrogate marker of a ‘good’ explanation, regardless of what is actually being said. In truth, neuroscience information is usually only decorative and is irrelevant to the explanation’s logic.

I also argue that neuroscience language is sometimes used not just to add weight to an argument but to divert the reader from weaknesses in an argument. People tend to think that longer explanations are a sign of expertise and when a writer presents related but logically irrelevant details to an argument it makes it more difficult for the reader to encode, and therefore later recall, the main argument of a text because their attention is diverted by ‘seductive details’.

And I argue that neuroscience is used as a reductionist explanation about the world. In other words, when we read neuroscience language in a bogus neuroscience explanation, we feel as if we have been given a physical explanation for a behavioural phenomenon. The use of neuroscience makes behavioural phenomena feel connected to the physical sciences and, therefore, a certainty supported by graphs and data.

My unease is not new: I warned here that we should be discerning in our use of evidence in education. 

UPDATE: The articles I mention above – is neuroscience the next cargo-cult in education? – have now been published and are available on my blog. Part One is here and Part Two is here

*

Further reading:

I highlight in bold the papers/books I would particularly recommend:

Agarwal, P. K., Bain, P. M., & Chamberlain, R. W. (2012). The value of applied research: Retrieval practice improves classroom learning and recommendations from a teacher, a principal, and a scientist. Educational Psychology Review, 24(3), 437-448.

Agodini, R., Harris, B., Atkins-Burnett, S., Heaviside, S., Novak, T., & Murphy, R. (2009). Achievement Effects of Four Early Elementary School Math Curricula: Findings from First Graders in 39 Schools. NCEE 2009-4052. National Center for Education Evaluation and Regional Assistance.

Ainsworth, S., Bibby, P., & Wood, D. (2002). Examining the Effects of Different Multiple Representational Systems in Learning Primary Mathematics. The Journal of the Learning Sciences, 11(1), 25-61.

Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from Examples: Instructional Principles from the Worked Examples Research. Review of Educational Research, 70(2), 181-214.

Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78(1), 246-263.

Bottge, B. A., Rueda, E., Serlin, R. C., Hung, Y.-H., & Kwon, J. M. (2007). Shrinking Achievement Differences With Anchored Math Problems. The Journal of Special Education, 41(1), 31-49.

Boyd, R. (2008, February 7). Do People Only Use 10 Percent of Their Brains? Retrieved March 7, 2015, from Scientific American: http://www.scientificamerican.com/article/do-people-only-use-10-percent-of-their-brains/

Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How People Learn: Brain, Mind, Experience, and School. Washington, DC: National Academy Press.

Burnette, J. L., O’Boyle, E. H., VanEpps, E. M., Pollack, J. M., & Finkel, E. J. (2013). Mind-sets matter: A meta-analytics review of implicit theories and self-regulation. Psychological Bulletin, 139(3), 655-701.

Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245-281.

Cameron, J., Banko, K. M., & Pierce, W. D. (2001). Pervasive negative effects of rewards on intrinsic motivation: The myth continues. The Behavior Analyst, 24(1), 1-44.

Catrambone, R. (1996). Generalizing solution procedures learned from examples. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(4), 1020-1031.

Catrambone, R. (1998). The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General, 127(4), 355-376.

Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed Practice in Verbal Recall Tasks: A Review and Qualitative Synthesis. Psychological Bulletin, 132(3), 354-380.

Chandler, P., & Sweller, J. (1992). The Split-Attention Effect as a Factor in the Design of Instruction. British Journal of Educational Psychology, 62(2), 233-246.

Cohen, G., Steele, C., & Ross, L. (1999). The Mentor’s Dilemma: Providing Critical Feedback Across the Racial Divide. Personality and Social Psychology Bulletin, 25(10), 1302-1318.

Davis, K. D., Winsler, A., & Middleton, M. (2006). Students’ perceptions of rewards for academic performance by parents and teachers: Relations with achievement and motivation in college. Journal of Genetic Psychology, 167(2), 211-220.

Day, S. B., & Goldstone, R. L. (2012). The import of knowledge export: Connecting findings and theories of transfer of learning. Educational Psychologist, 47(3), 153-176.

Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627-668.

Elliott, E. S., & Dweck, C. S. (1988). Goals: An approach to motivation and achievement. Journal of Personality and Social Psychology, 54(1), 5-12.

Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The Role of Deliberate Practice in the Acquisition of Expert Performance. Psychological Review, 100(3), 363-406.

EU High Level Group of Experts on Literacy. (2012). Final Report. Luxembourg: Publications Office of the European Union.

Flynn, E., O’Malley, C., & Wood, D. (2004). A longitudinal, microgenetic study of the emergence of false belief understanding and inhibition skills. Developmental Science, 7(1), 103-115.

Gentner, D., Levine, S. C., Dhillon, S., Ping, R., Bradley, C., Poltermann, A., et al. (2015). Rapid learning in a children’s museum via analogical comparison [in press]. Cognitive Science.

Glaser, R., & Chi, M. T. (1988). Overview. In The Nature of Expertise (pp. xv-xxvii). Hillsdale: Erlbaum.

Goldstone, R. L., & Son, J. Y. (2005). The Transfer of Scientific Principles using Concrete and Idealized Simulations. Journal of the Learning Sciences, 14(1), 69-110.

Graesser, A. C., & Olde, B. A. (2003). How does one know whether a person understands a device? The quality of the questions the person asks when the device breaks down. Journal of Educational Psychology, 95(3), 524-536.

Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81-112.

Kamins, M. L., & Dweck, C. S. (1999). Person versus process praise and criticism: implications for contingent self-worth and coping. Developmental Psychology, 35(3), 835-847.

Karpicke, J. D., Butler, A. C., & Roediger, H. L. (2009). Metacognitive strategies in student learning: Do students practise retrieval when they study on their own? Memory, 17(4), 471-479.

Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why Minimal Guidance During Instruction Does Not Work. Educational Psychologist, 41(2), 75-86.

Koriat, A. (1993). How do we know that we know? The accessibility model of the feeling of knowing. Psychological Review, 100(4), 609-639.

Koriat, A., & Levy-Sadot, R. (2001). The combined contributions of the cue-familiarity and accessibility heuristics to feelings of knowing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(1), 34-53.

Levitt, S. D., List, J. A., & Neckermann, S. S. (2012). The Behavioralist Goes to School: Leveraging Behavioral Economics to Improve Educational Performance (NBER Working Paper, 18165). National Bureau of Economic Research.

McDaniel, M. A., Hines, R. J., Waddill, P. J., & Einstein, G. O. (1994). What makes folk tales unique: Content familiarity, causal structure, scripts, or superstructures? Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(1), 169-184.

Moreno, R. (2006). Learning in High-Tech and Multimedia Environments. Current Directions in Psychological Science, 15(2), 63-67.

Moreno, R., & Mayer, R. E. (1999). Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity. Journal of Educational Psychology, 91(2), 358-368.

Morris, C. D., Bransford, J. D., & Franks, J. J. (1977). Levels of Processing Versus Transfer Appropriate Processing. Journal of Verbal Learning and Verbal Behavior, 16(5), 519-533.

Mueller, C. M., & Dweck, C. S. (1998). Praise for intelligence can undermine children’s motivation and performance. Journal of Personality and Social Psychology, 75(1), 33-52.

National Mathematics Advisory Panel. (2008). Foundations for Success: The Final Report of the National Mathematics Advisory Panel. Washington, DC: U.S. Department of Education.

National Reading Panel. (2000). Teaching Children to Read: Reports of the Subgroups. National Institute of Child Health and Human Development.

Nielson, J. A., Zielinski, B. A., Ferguson, M. A., Lainhart, J. E., & Anderson, J. S. (2013). An Evaluation of the Left-Brain vs Right-Brain Hypothesis with Resting State Functional Connectivity Magnetic Resonance Imaging. PLOS ONE, 8(8).

Pashler, H., Bain, P. M., Bottge, B. A., Graesser, A., Koedinger, K., & McDaniel, M. (2007). Organizing Instruction and Study to Improve Student Learning. U.S. Department of Education. Washington DC: National Center for Education Research, Institute of Education Sciences.

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

Pellegrino, J. W., & Hilton, M. L. (2012). Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. Washington, DC: National Academies Press.

Peters, E. E., & Levin, J. R. (1986). Effects of a mnemonic imagery strategy on good and poor readers’ prose recall. Reading Research Quarterly, 21(2), 179-192.

Richland, L. E., Zur, O., & Holyoak, K. J. (2007). Cognitive Supports for Analogies in the Mathematics Classroom. Science, 316(5828), 1128-1129.

Rohrer, D., Dedrick, R. F., & Stershic, S. (2015). Interleaved practice improves mathematics learning. Journal of Educational Psychology, 107(3), 900-908.

Rosenshine, B., Meister, C., & Chapman, S. (1996). Teaching Students to Generate Questions: A Review of the Intervention Studies. Review of Educational Research, 66(2), 181-221.

Shute, V. J. (2008). Focus on Formative Feedback. Review of Educational Research, 78(1), 153-198.

Siegler, R. S. (1995). How does change occur: A microgenetic study of number conservation. Cognitive Psychology, 28(3), 225-273.

Smiley, P. A., & Dweck, C. S. (1994). Individual differences in achievement goals among young children. Child Development, 65(6), 1723-1743.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.

Sweller, J. (2006). The Worked Example Effect and Human Cognition. Learning and Instruction, 16(2), 165-169.

Sweller, J., van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296.

TeachingWorks. (n.d.). High-Leverage Practices. Retrieved March 7, 2015, from http://www.teachingworks.org/work-of-teaching/high-leverage-practices

Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331, 1447-1451.

Waterhouse, L. (2006). Multiple Intelligences, the Mozart Effect, and Emotional Intelligence: A Critical Review. Educational Psychologist, 41(4), 207-225.

Willingham, D. T. (2004). Reframing the Mind: Howard Gardner and the theory of multiple intelligences. Education Next, 4(3), 19-24.

Willingham, D. T. (2008, Summer). What is Developmentally Appropriate Practice? American Educator, pp. 34-39.

Willingham, D. T. (2009). Why Don’t Students Like School? San Francisco, CA: Jossey-Bass.

Yeager, D. S., Johnson, R., Spitzer, B. J., Trzesniewski, K. H., Powers, J., & Dweck, C. S. (2014). The far-reaching effects of believing people can change: Implicit theories of personality shape stress, health, and achievement during adolescence. Journal of Personality and Social Psychology, 106(6), 867-884.

Yeager, D., Purdie-Vaughns, V., Garcia, J., Apfel, N., Brzustoski, P., Master, A., et al. (2014). Breaking the Cycle of Mistrust: Wise Interventions to Provide Critical Feedback Across the Racial Divide. Journal of Experimental Psychology, 143(2), 804-824.

Yeager, D., Walton, G., & Cohen, G. L. (2013, February). Addressing achievement gaps with psychological interventions. Phi Delta Kappan, 62-65.

 

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