New Approaches from Cognitive Science to Learning and Education

Psychology 747, Section 11494 - Fall 2013

Room 128 Psychology Building, 1101 E. 10th St.

Meeting Time: Wednesdays, 10:00 AM-12:00 noon

Instructor: Professor Robert Goldstone

Office: 338 Psychology

Office hours: Wednesday 2:00-3:30

Phone: 855-4853

Email: rgoldsto@indiana.edu

Web site: http://cognitrn.psych.indiana.edu/rgoldsto/courses/cogscilearning

 

 

Course Description

One of the most exciting application areas for cognitive science is improving learning outcomes.  Decades of research in cognitive science has given us a rigorous understanding of the mechanisms that underlie learning at neural and functional levels.  Although we lack a complete understanding of learning, particularly in real world contexts, it is not premature for us to work towards applying the fruits of basic research on learning to educational and training settings.  This seminar will explore new approaches from cognitive science to understanding and promoting learning and education.  Topics in computer science, developmental psychology, and education will be covered, but the perponderance of the material will be from cognitive psychology.

 

The four principle obligations of seminar participants will be to 1) lead one of the 13 class discussions, 2) read the weekly assignment each week, 3) actively participate in all class discussions, and 4) prepare a final project that teaches somebody something using principles covered in the seminar.  To facilitate activities 2) and 3), participants are required to either prepare a one-page written reaction to the weekly readings or to respond to the reaction of another student.  Given the occasionally overwhelming pressures on students, participants are exempted from preparing reaction pages for two seminars of their choice.  Thus, you must prepare a reaction or reaction-reaction for 11 of the weeks.  These should be evenly divided into 6 reactions, and 5 reaction-reactions.  Reactions will be coarsely graded (unacceptable, acceptable, and outstanding) and will receive brief comments by me.

 

 

Leading a Seminar

 

The purpose of the seminar leader is two-fold - to review the fundamental points of the readings, and to generate and direct active discussion.  You should prepare about 25 minutes of instructional monologue.  Powerpoint slides and/or handouts are encouraged.  You should assume that everybody has read the material, but you may want to explain aspects of the papers that other students could have difficulty understanding.  Do not attempt to cover all of the material in detail.  Rather, select a handful of points that seem to be of fundamental importance.  You may decide to either discuss each of the readings separately, or as one integrated whole.  Consider time to be a precious resource; do not waste it on digressions.  Two ingredients of a successfully run seminar are that the leader focuses his or her comments on critical themes in the material, and opens up discussion so that the seminar participants are actively involved.

 

Reaction Pages

Late reaction pages will not be accepted (the point of the reaction page is to have participants think about their reaction before the seminar).  You will submit your reaction pages using the web-based Annotate system developed by Indiana University's cognitive science program.  This system is accessed at:

http://www.indiana.edu/~annotate/

Annotate has been designed so that students can read each other's reactions, add their comments to the reaction, comment on other students' comments, etc.  I will also make comments that can be read by all students, and assign grades that can be read by only the receiving student.  Reaction pages will be coarsely graded (check minus = unacceptable, check = acceptable, and check plus = outstanding).  The most common grade is "check," and do not be surprised if most of your reactions are not rated as "outstanding."  I reserve this grade for truly noteworthy and insightful contributions.

 

The purpose of the weekly reaction page requirement is for seminar participants to develop particular perspectives on their readings.  As E. M. Forester said, “How can I know what I think until I see what I say [write]?”  The act of writing forces thoughts to be more precise and organized than they would otherwise be.  The assignment is purposefully open-ended.  Appropriate topics for reaction pages may be suggested, but most often, you will be left to select for yourself an interesting topic that relates to the readings in some way.

 

Once again, space should be considered a scarce resource.  You should try to refine your thoughts such that they can be concisely expressed on a single page.  The most successful reaction pages focus on a single topic.  Resist the temptation to write a few sentences each on four topics.

 

What are appropriate topics for reaction pages?  You may develop an experiment that is inspired by one of the readings.  Describe the experiment briefly, explain how it bears on relevant theories, and make predictions on the results.  You may disagree with a particular claim.  Explain why the claim is wrong, and why it is important that it is wrong.  You may agree with a claim.  Describe extensions to the claim, possible applications of the research to the classroom or informal learning contexts, formal models that capture the essence of the claim, or future directions for research.  You may have nothing to say about a particular article.  If so, explain why the article is not relevant to fundamental issues of learning or education.  Discuss the assumptions of the article, and why you find them inappropriate.  Generally speaking, organizing your reaction page around a claim rather than a question stimulates more interest.

 

On the weeks when you choose to react to a reaction page, you should submit your reaction with 7 days of the relevant seminar.  In your reaction-reaction, you can perform any of the intellectual moves described in the preceding paragraph.  You are encouraged to respond to as many of the reactions as you wish, but for one of your 5 reaction-reactions that count toward your grade, it must be on a different topic than one for which you already prepared a reaction.  Reaction-reactions are usually expected to be about .5-1 pages long.

 

Designing and Testing a Method for Teaching Somebody Something

 

Given the core thesis of the seminar that cognitive science is useful for understanding how people learn, and how to make that learning more efficient or successful, it is befitting that seminar participants actually take a stab at implementing a method for teaching somebody something that has been informed by the seminar readings.  For this project, you should: 1) first come up with something (a topic, a skill, a system, or a concept) that you would like to teach somebody, 2) devise your own method for instruction based on cognitive science principles, 3) give your method of instruction to at least 2-3 appropriate learners and evaluate their learning, 4) evaluate the success of your method, and how it might be modified, extended, or improved.  Your last week’s reaction page should describe these 4 steps.  In the final week of the course, participants give short 10-15 minute descriptions of these projects.  For Step 2, you might consider creating an on-line or automatic system of instruction and testing, but it is also perfectly fine to employ standard lecture techniques, direct instruction, open-ended exploration, Socratic dialogs with students, interactive coaching, or demonstration – whatever your understanding of cognitive science leads you to believe will be a promising approach.  Don’t get too carried away with Step 4 of the project.  You aren’t being asked to fully test the benefits of your approach, but just get a feeling for how your method bears up when actually deployed with learners.

 

Grading

Grades will be based on the quality of reaction pages, seminar leading, seminar participation, and final project.  To get a good participation evaluation, it is not necessary to make many comments.  Rare but thoughtful comments suffice.  Here is the breakdown of the requirements for different grades:

A: Hands in acceptable or outstanding reactions for 11-13 out of 13 weeks.  A strong final project that implements the 4 steps above, and is clearly guided by cognitive science principles.  Good discussion leading and participation.

B: hands in acceptable reactions for 9-10 out of 13 weeks.  A strong final project.  Good discussion leading and participation.

C: hands in acceptable reactions for 7-8 weeks.

 

 

 

 

 

 

Weekly readings

Readings in bold are required, the others are optional, but should be perused by the seminar leader.  There are more topics than there are weeks.  People will choose topics, and topics that are not picked by anybody will be left aside.  There are 22 topics listed below and only 13 sessions, so the competition among the topics to have a hearing will be keen!

 

See the UPDATED Schedule

 

Introduction

Optional readings

Roediger, H. L. (2012). Applying Cognitive Psychology to Education: Translational Educational Science. Psychological Science in the Public Interest, 14, 1–3.

Pashler, H., Bain, P., Bottge, B., Graesser, A., Koedinger, K., McDaniel, M., and Metcalfe, J. (2007). Organizing Instruction and Study to Improve Student Learning (NCER 2007-2004). Washington, DC: National Center for Education Research, Institute of Education Sciences, U.S. Department of Education. Retrieved from http://ncer.ed.gov.

 

Neuroscience and education

Core readings

Varma, S., McCandliss, B. D., & Schwartz, D. L. (2008).  Scientific and pragmatic challenges for bridging education and neuroscience.  Educational Researcher, 37, 140-152.

Dehaene, S., Pegado, F., Braga, L. W., Ventura, P., Filho, G. N., Jobert, A., Dehaene-Lambertz, G., Kolinsky, R., Morais, J., & Cohen, L. (2010).  How Learning to Read Changes the Cortical Networks for Vision and Language.  Science, 330, 1359-1364.

Anderson, J. R., Betts, S., Ferris, J. L, & Fincham, J. M. (2010).  Neural imaging to track mental states while using an intelligent tutoring system. Proceedings of the National Academy of Sciences, 107, 7018-7023.

Optional readings

Dehaene, S., Molko, N., Cohen, L., Wilson, A. J. (2004).  Arithmetic and the brain.  Current opinion in Neurobiology, 14, 218-224.

McCandliss, B. D. (2010). Educational neuroscience: The early years. PNAS, 107, 8049-8050.

Nieder, A., & Dehaene, S. (2009). Representation of number in the brain. Annual Review of Neuroscience, 32, 185–208.

           

Perception and education

Core readings

Uttal, D. H., & Cohen, C. A. (2012).  Spatial thinking and STEM education: When, why, and how?  Psychology of Learning and Motivation, 57, 147-181.

Kellman, P. J., Massey, C. M., Son, J. Y. (2009).  Perceptual Learning Modules in Mathematics: Enhancing Students’ Pattern Recognition, Structure Extraction, and Fluency,  Topics in Cognitive Science, 2, 285-305.

Goldstone, R. L., Landy, D. H., & Son, J. Y. (2010).  The education of perception. Topics in Cognitive Science, 2, 265-284.

Optional readings

Kellman, P. J., & Massey, C. M. (2013).  Perceptual Learning, Cognition, and Expertise.  Psychology of Learning and Motivation, 58, 117-165.

 

Perceptual Learning

Core readings

Goldstone, R. L. (1998).  Perceptual Learning.  Annual Review of Psychology, 49, 585-612.

Kourtzi, Z., & Connor, C. E. (2011). Neural representations for object perception: structure, category, and adaptive coding. Annual review of neuroscience, 34, 45–67.

G. Hall. Perceptual Learning. In R. Menzel (Ed.), Learning Theory and Behavior. Vol. [1] of Learning and Memory: A Comprehensive Reference, 4 vols. (J.Byrne Editor), pp. [103-122] Oxford: Elsevier.

Optional readings

Petrov, A. A., Dosher, B. A., & Lu, Z.  (2005).  The Dynamics of Perceptual Learning: An Incremental Reweighting Model.  Psychological Review, 112, 715-743.

Edelman, S. & Intrator, N. (2002).  Models of perceptual learning. in Perceptual learning, M. Fahle and T. Poggio, eds., MIT Press.

McClelland, J. L., Fiez, J.A. & McCandliss, B. D. (2002). Teaching the /r/-/l/ discrimination to Japanese adults: behavioral and neural aspects. Physiology & Behavior, 77, 657-62.

 

Motor and skill learning

Core readings

Ericsson, K. A., Krampe, R. T, & Tesch-Römer, C. (1993).  The role of deliberate practice in the acquisition of expert performance.  Psychological Review, 100, 363-406.

Wulf, G., & Shea, C. H. (2002).  Principles derived from the study of simple skills do not generalize to complex skill learning.  Psychonomic Bulletin & Review, 9, 185-211.

Guadagnoli, M. A., & Lee. T. D. (2004).  Challenge Point: A Framework for Conceptualizing the Effects of Various Practice Conditions in Motor Learning. Journal of Motor Behavior, 36, 212-224.

Optional readings

Brydges, R., Carnahan, H., Backstein, D., & Dubrowski, A. (2007).  Application of Motor Learning Principles to Complex Surgical Tasks: Searching for the Optimal Practice Schedule.  Journal of Motor Behavior, 39, 40-48.

Willingham, D. B. (1998).  A neurophysiological theory of motor skill learning.  Psychological Review, 105, 558-584.

 

Concreteness and manipulatives

Core readings          

Uttal, D. H., Scudder, K. V., & DeLoache, J. S. (1997). Manipulatives as symbols: a new perspective on the use of concrete objects to teach mathematics. Journal of Applied Developmental Psychology, 18, 37-54.

Kaminski, J. A., Sloutsky V. M., & Heckler, A. F. (2008). The advantage of abstract examples in learning math. Science, 320, 454-455.

Brown, M. C., McNeil, N. M., & Glenberg, A. M. (2009). Using concreteness in education: Real problems, potential solutions. Child Development Perspectives, 3(3), 160–164.

Optional readings

McNeil, N. M., & Fyfe, E. R. (2012).  "Concreteness Fading" promotes transfer of mathematical knowledge.  Learning and Instruction, 22, 440-448.

Goldstone, R. L., & Sakamoto, Y. (2003). The Transfer of Abstract Principles Governing Complex Adaptive Systems.  Cognitive Psychology, 46, 414-466.

de Bock, D., Deprez, J., van Dooren, W., & Roelens, M. (2011).  Abstract or Concrete Examples in Learning Mathematics? A Replication and Elaboration of Kaminski, Sloutsky, and Heckler’s Study.  Journal for Research in Mathematics Education, 42, 109-126.

McNeil, N. M., Uttal, D. H., Jarvin, L., & Sternberg, R. J. (2009).  Should you show me the money? Concrete objects both hurt and help performance on mathematics problems.  Learning and Instruction, 19, 171-184.

 

Visualization and Modeling

Core readings

Mayer, R. E. (2011).  Applying the science of learning to multimedia instruction.  Psychology of Learning and Motivation, 55, 77-108.

Catrambone, R., Craig, D. L., & Nersessian, N. J. (2006). The role of perceptually represented structure in analogical problem solving. Memory & Cognition, 34(5), 1126–32.

Ainsworth, S., Prain, V., & Tyler, R. (2011).  Drawing to learn in science.  Science, 333, 1096-1097.

Optional readings

Glenberg, A. M., Gutierrez, T., Levin, J. R., Japuntich, S., & Kaschak, M. P.  (2004). 

     Activity and imagined activity can enhance young children's reading comprehension. Journal of Educational Psychology, 96, 424-436.

Zahner, D., & Corter, J. E. (2010).  The Process of Probability Problem Solving: Use of External Visual Representations.  Mathematical Thinking and Learning, 12, 177-204.

 

Embodied cognition and education

Core readings

Martin, T., & Schwartz, D. L. (2005).  Physically Distributed Learning: Adapting and Reinterpreting Physical Environments in the Development of Fraction Concepts.   Cognitive Science, 29, 587-625.

Goldin-Meadow, S.  (2004).  Gesture's role in the learning process.  Theory into Practice, 43, 314-321.

Alibali, M. W., & Nathan, M. J. (2011). Embodiment in Mathematics Teaching and Learning: Evidence From Learners' and Teachers' Gestures, Journal of the Learning Sciences, DOI:10.1080/10508406.2011.611446

Optional readings

Andres, M., Olivier, E., & Badets, A. (2008).  Actions, words, and numbers.  A motor contribution to semantic processing?  Current Directions in Psychological Science, 17, 313-317.

 

Formalisms

Core readings

Schwartz, D. L., & Black, J. B. (1996).  Shuttling between depictive models and abstract rules: Induction and fallback.  Cognitive Science, 20, 457-497.

Nathan, M. J. (2012). Rethinking formalisms in formal education. Educational Psychologist, 47(2), 125-148.

Koedinger, K. R., Alibali, M. W., & Nathan, M. J. (2008).  Trade-offs between grounded and abstract representations: Evidence from algebra problem solving.  Cognitive Science, 32, 366-397.

Optional readings

Landy, D. & Goldstone, R. L. (2007). How abstract is symbolic thought? Journal of Experimental Psychology: Learning, Memory, & Cognition, 33, 720-733.

 

Procedural and Conceptual Knowledge

Core readings

Schneider, M., Rittle-Johnson, B., & Star, J. (2011). Relations between conceptual knowledge, procedural knowledge, and procedural flexibility in two samples differing in prior knowledge. Developmental Psychology, 47, 1525-1538.

McNeil, N. M., & Alibali, M. W. (2005).  Why Won’t You Change Your Mind? Knowledge of Operational Patterns Hinders Learning and Performance on Equations.  Child Development, 76, 883-899.

Dixon, J. A., & Bangert, A. S. (2004). On the spontaneous discovery of a mathematical relation during problem solving.  Cognitive Science, 28, 433-449.

Optional readings

Star, J. R. (2005).  Reconceptualizing procedural knowledge.  Journal of Research in Mathematics Education, 36, 404-411.

 

Analogy and comparison

Core readings

Hofstadter, D. & Sander, E. (2013). Surfaces and Essences: Analogies as the Fuel and Fire of Thinking. New York: Basic Books.  (Chapter 7)

Holyoak, K. J. (2012). Analogy and relational reasoning. In K. J. Holyoak & R. G. Morrison (Eds.), The Oxford handbook of thinking and reasoning (pp. 234-259). New York: Oxford University Press.

Gentner, D. (2010). Bootstrapping the Mind: Analogical Processes and Symbol Systems.  Cognitive Science, 34, 752-775.

Rittle-Johnson, B., & Star, J. (2011).  The Power of Comparison in Learning and Instruction: Learning Outcomes Supported by Different Types of Comparisons.  Psychology of Learning and Motivation, 55, 200-225.

Optional readings

Richland, L. E., Zur, O., & Holyoak, K. J. (2007).  Science, 316, 1128-1129

Day, S. B., & Goldstone, R. L. (2011).  Analogical transfer from a simulated physical system.  Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 551-567.

Gentner, D., Loewenstein, J., & Thompson, L. (2003).  Learning and transfer: A general role for analogical encoding.  Journal of Educational Psychology, 95, 393-408.

Rittle-Johnson, B., & Star, J. R. (2007). Does comparing solution methods facilitate conceptual and procedural knowledge? An experimental study on learning to solve equations. Journal of Educational Psychology, 99(3), 561–574.

 

Transfer

Core readings

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

Koedinger, K. R., & Roll, I. (2012). Learning to think: Cognitive mechanisms of knowledge transfer. In K. J. Holyoak, & R. G. Morrison (Eds.), The Oxford handbook of thinking and reasoning (2nd ed.). New York: Cambridge University Press (pp. 789-806).

Schwartz, D. L., Bransford, J. D., & Sears, D. (2005).  Efficiency and innovation in transfer.  Transfer of learning from a modern multidisciplinary perspective.  1-51.

Optional readings

Full “New Conceptualization of Transfer of Learning” issue of Educational Psychologist, with articles by Joanne Lobato, David Perkins, Keith Holyoak, Daniel Schwartz, Randi Engle, Xenia Meyer, Sarah Nix, Diane Lam, Michilene Chi, James Stigler, Cathy Chase, John Bransford, Gavriel Salomon

Nokes, T. J. (2009).  Mechanisms of knowledge transfer.  Thinking & Reasoning, 15, 1-36.

Schwartz, D. L., & Martin, T. (2004).  Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction.  Cognition and Instruction, 22, 129-184.

 

Science learning

Core readings

Smith III, J. R., diSessa, A. A., & Roschelle, J. (1994).  Misconceptions Reconceived: A Constructivist Analysis of Knowledge in Transition.  Journal of the Learning Sciences, 3, 115-163.

Mestre, J. P, Docktor, J. L., Strand, N. E., & Ross, B. H. (2011).  Conceptual problem solving in physics.   Psychology of Learning and Motivation, 55, 270-298.

Lindgren, R., & Schwartz, D. L. (2009).  Spatial Learning and Computer Simulations in Science.  International Journal of Science Education, 31, 419-438.

Optional readings

Lombrozo, T., Thanukos, A., & Weisberg, M. (2008). The importance of understanding the nature of science for accepting evolution. Evolution: Education & Outreach, 1, 290-298.

Klahr, D., Triona, L. M., & Williams, C. (2007) Hands On What? The Relative Effectiveness of Physical vs. Virtual Materials in an Engineering Design Project by Middle School Children. Journal of Research in Science Teaching , 44, 183-203

de Jong, T., Linn, M. C., & Zacharia, A. C. (2013).  Physical and Virtual Laboratories in Science and Engineering Education.  Science, 340, 305-308.

 

Math learning

Core readings

Lakoff, G., & Nunez, R. E. (2000). Where mathematics comes from: How the embodied mind brings mathematics into being. New York: Basic Books.  (pp. 1-103).

Siegler, R. S. (2003). Implications of cognitive science research for mathematics education. In Kilpatrick, J., Martin, W. B., & Schifter, D. E. (Eds.), A research companion to principles and standards for school mathematics (pp. 219-233).

Halberda, J., Mazzocco, M. M., & Feigenson, L. (2008). Individual differences in non-verbal number acuity correlate with maths achievement. Nature, 455, 665–668.

Optional readings

Marghetis, T., & NúĖez, R. (2013).  The Motion Behind the Symbols: A Vital Role for Dynamism in the Conceptualization of Limits and Continuity in Expert Mathematics.  Topics in Cognitive Science.

Siegler, R. S., Fazio, L. K., Bailey, D. H., & Zhou, X. (2013).  Fractions: the new frontier for theories of numerical development.  Trends in Cognitive Science, 17, 13-19.

Sfard, A., & Lavie, I. (2005).  Why Cannot Children See as the Same What Grown-Ups Cannot See as Different?– Early Numerical Thinking Revisited.  Cognition and Instruction, 23, 237-309.

 

Systems thinking

Core readings

Goldstone, R. L., & Wilensky, U. (2008).  Promoting Transfer through Complex Systems Principles. Journal of the Learning Sciences, 17, 465-516.

Chi, M. T. H., Roscoe, R., Slotta, J. D., & Roy, M.  (2012). Misconceived Causal Explanations for Emergent Processes.  Cognitive Science, 36, 1-61.

Hmelo-Silver, C. E., Matathe, S., & Liu, l. (2007).  Fish Swim, Rocks Sit, and Lungs Breathe: Expert-Novice Understanding of Complex Systems.  Journal of the Learning Science, 16, 307-331.

Optional readings

Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15, 11–34.

Rottman, B.M., & Keil, F.C. (2011). What matters in scientific explanations: Effects of elaboration and content, Cognition, 121, 324-37.

 

Memory and retrieval

Core readings

Roediger III, H. L., Putnam, A. L., & Smith, M. A. (2011).  Ten benefits of testing and their application to educational practice. Psychology of Learning and Motivation, 55, 1-36.

Karpicke, J. D. & Blunt, J. R. (2011). Retrieval practice produces more learning than elaborate studying with concept mapping. Science, 331, 772-775

Loewenstein, J. (2010). How one’s hook is baited matters for catching an analogy. In B. Ross (Ed.), Psychology of Learning and Motivation, Volume 53. Elsevier.

Optional readings

Carpenter, S. K., Testing enhances the transfer of learning.  Current Directions in Psychological Science, 21, 279-283.

 

Spacing and sequencing of learning materials

Core readings

Cepeda, N. J., Coburn, N., Rohrer, D., Wixted, J. T., Mozer, M. C., & Pashler, H. (2009).  Optimizing distributed practice.  Experimental Psychology, 56.

Kornell N, Bjork R. A. (2008). Learning concepts and categories: Is spacing the “enemy of induction”? Psychological Science, 19, 585–592.

Novikoff, T. P., Kleinberg, J. M., & Strogatz, S. H. (2012).  Education of a model student.  Proceedings of the National Academy of Sciences, 109,  1868-1873.

Optional readings

Pavlik, P.I. & Anderson, J. R. (2008). Using a model to compute the optimal schedule of practice. Journal of Experimental Psychology: Applied, 14, 101-117.

 

Metacognition and self-regulated learning

Core readings

Bjork, R. A., Dunlovsky, J., & Kornell, N. (2013).  Self-regulated learning: Beliefs, techniques, and illusions.  Annual Review of Psychology, 64, 417-444.

Metcalfe, J. (2009).  Metacognitive judgments and control of study.  Current Directions in Psychological Science, 18, 159-163.

Benjamin, A. S., Bjork, R. A., & Schwartz, B. L. (1998). The mismeasure of memory: When retrieval fluency is misleading as a metamnemonic index. Journal of Experimental Psychology: General, 127, 55-68.

Chi, M.T.H., de Leeuw, N., Chiu, M.H., LaVancher, C. (1994).  Eliciting self-explanations improves understanding.  Cognitive Science, 18, 439-477.

 

Optional readings

Son, L. K. & Metcalfe, J. (2000). Metacognitive and control strategies in study-time allocation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 204-221.

Aleven, V. A. W. M. M., & Koedinger, K. R. (2002).  An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor.  Cognitive Science, 26, 147-179.

 

Scaffolds in learning

Core readings

Koedinger, K.R., & Aleven, V. (2007). Exploring the Assistance Dilemma in experiments with Cognitive Tutors. Educational Psychology Review, 19(3), 239-264.

Pea, R.D. (2004).  The social and technological dimensions of scaffolding and related theoretical concepts for learning, education, and human activity.  The Journal of the Learning Sciences, 13 (3), 423-451.

van MerriĎnboer, J. J. G., Kirschner, P. A., & Kester, L.  (2003).  Taking the load off a learner's mind: Instructional design for complex learning.  Educational Psychologist, 38, 5-13.

Optional readings

Sherin, B., Reiser, B. J., & Edelson, D.  (2004).  Scaffolding Analysis: Extending the Scaffolding Metaphor to Learning Artifacts, 13, 387-421.

    

Direct Instruction and Discovery Learning

Core readings          

Kirshner, P.A., Sweller, J., & Clark, R.E. (2006).  Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching.  Educational Psychologist, 41 (2), 75-86.

Klahr, D., & Nigam, M. (2004).  The equivalence of learning paths in early science instruction: Effect of direct instruction and discovery learning.  Psychological Science, 15, 661-667.

Hmelo-Silver, C.E., Duncan, R.G., & Chinn, C.A. (2006).  Scaffolding and achievement in problem-based and inquiry learning:  A response to Kirschner, Sweller, and Clark.  Educational Psychologist, 43 (2), 99-107.

Optional readings

Swaak, J., de Jong, T., van Joolingen, W. R. (2004).  The effects of discovery learning and expository instruction on the acquisition of definitional and intuitive knowledge.  Journal of Computer Assisted Learning, 20, 225-234.

Chi, M. T. (2009).  Active-Constructive-Interactive: A Conceptual Framework for Differentiating Learning Activities.  Topics in Cognitive Science, 1, 73-105.

 

Models of learning

Core Readings

Anderson, J. R. (2005) Human symbol manipulation within an integrated cognitive architecture. Cognitive Science, 29(3), 313-341.

Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012).  The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning, Cognitive Science, 36, 757-798.

Matsuda, N., Cohen, W. W., Sewall, J., Lacerda, G., & Koedinger, K. R. (2007). Predicting students performance with SimStudent that learns cognitive skills from observation. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Proceedings of the international conference on Artificial Intelligence in Education (pp. 467-476). Amsterdam, Netherlands: IOS

Matsuda, N., Keiser, V., Raizada, R., Tu, A., Stylianides, G., Cohen, W. W., et al. (2010). Learning by Teaching SimStudent: Technical Accomplishments and an Initial Use with Students. In V. Aleven, J. Kay & J. Mostow (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 317-326). Heidelberg, Berlin: Springer.

Optional readings

Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95, 492-527.

Stoianov, I., & Zorzi, M. (2012).   Emergence of a ‘visual number sense’ in hierarchical generative models,  Nature Neuroscience, 15, 194-196.

 

Practical recommendations for improving learning outcomes

Core readings

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013).  Improving students' learning with effective learning techniques: Promising directions from cognitive and educational psychology, Psychological Science in the Public Interest, 14, 4-58.

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009).  Learning Styles.  Psychological Science in the Public Interest, 9, 105-119.

Lovett, M. C., & Greenhouse, J. B. (2000).  Applying cognitive theory to statistics instruction.  The American Statistician, 54, 1-11.

Optional readings

Van Merrienboer, J.J.G., Clark, R.E., & de Croock, M.B.M (2002).  Blueprints for complex learning.  Educational Technology Research and Development, 50 (2), 39-64.

 

Proposing systems that improve learning

Core Readings

Ritter, S., Anderson, J. R., Koedinger, K. R., & Corbett, A. (2007) Cognitive Tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14, 249-255.

Biswas, G., Leelawong, K., Schwartz, D., Vye, N., & The Teachable Agents Group at Vanderbilt (2005).  Learning by teaching: A new agent paradigm for educational software.  Applied Artificial Intelligence, 19, 363-392.

Ramani, G. B, Siegler, R. S., Hitti, A. (2012).  Taking it to the classroom: Number board games as a small group learning activity.  Journal of Educational Psychology, 104, 661-672.

Optional readings

Lillard, A., & Else-Quest, N. (2006).  Evaluating Montessori education.  Science, 313, 1893-1894.

Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4(2), 167-207.

Siegler, R. S. (2009).  Improving the Numerical Understanding of Children From Low-Income Families.  Child Development Perspectives, 3, 118-124.