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Research

Published research to describe the effectiveness
of our curricula.

Cognitive science research investigates how the mind works, and specifically how people learn. Carnegie Learning applies this research to improve the processes and outcomes of mathematics teaching and learning. The select research reports included here provide educators, administrators, assessment specialists and parents with important background information.

Click the headings below for detailed references.

  • Cognitive Psychology and ACT-R
    These references provide background information about the ACT-R theory of learning, memory and performance, which is the basis for Cognitive Tutor software. (27 references)
  • Cognitive Tutor Evaluation
    These references include formative and summative evaluations of Cognitive Tutor software. (30 references)
  • Cognitive Tutor Implementation
    These reference explore the technical implementation of Cognitive Tutor software. (29 references)
  • Theoretical Papers
    These references provide information about the basic pedagogical approach and theoretical background behind Cognitive Tutor software. (11 references)
  • Mathematics Education
    These references involve the basic research in mathematics education that was particularly influential in the development of Cognitive Tutor software. (19 references)

Cognitive Psychology and ACT-R

  • Anderson, J. R. (2000). Cognitive Psychology and Its Implications: Fifth Edition. New York: Worth Publishing.
  • Anderson, J. R. (2000). Learning and Memory, Second Edition. New York: Wiley.
  • Anderson, J. R. (1995). Cognitive Psychology and Its Implications: Fourth Edition. New York: Freeman.
  • Anderson, J. R. (1995). Learning and Memory. New York: Wiley.
  • Anderson, J. R. (1993). Problem solving and learning. American Psychologist, 48, 35-44.
  • Anderson, J. R. (1993). Rules of the Mind. Hillsdale, NJ: Erlbaum.
  • Anderson, J. R. (1992). Automaticity and the ACT* theory. American Journal of Psychology, 105, 165-180.
  • Anderson, J. R. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum.
  • Anderson, J. R. (1990). Cognitive Psychology and its Implications: Third Edition. New York: Freeman.
  • Anderson, J. R. (1985). Cognitive Psychology and its Implications (2nd Ed.). New York: Freeman.
  • Anderson, J. R. (1983). The Architecture of Cognition. Cambridge, MA: Harvard University Press.
  • Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369-403.
  • Anderson, J. R. (1980). Cognitive psychology and its implications. San Francisco: Freeman.
  • Anderson, J. R. (1976). Language, memory, and thought. Hillsdale, NJ: Erlbaum.
  • Anderson, J. R. & Bower, G. H. (1973). Human associative memory. Washington: Winston and Sons.
  • Anderson, J. R. & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Erlbaum. [Chapter abstracts, model source code, and web-based simulations] [info]
  • Anderson, J. R., Qin, Y., Sohn, M-H., Stenger, V. A. & Carter, C. S. (in press). An information-processing model of the ACT-R 5.0 web-based BOLD response in symbol manipulation tasks. Psychonomic Bulletin and Review. [ACT-R 5.0 web-based simulations and model source code] [info]
  • Blessing, S. & Anderson, J. R. (1996). How people learn to skip steps. Journal of Experimental Psychology: Learning, Memory and Cognition, 22, 576-598.
  • Blessing, S. B. & Ross, B. H. (1996). Content effects in problem categorization and problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 792-810.
  • Blessing, S. B. & Ross, B. H. (1994). Problem content affects the categorization and solutions of problems. In A. Ram and K. Eiselt (Eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society (pp. 51-55). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
  • Gunzelmann, G., & Anderson, J. R. (2001). An ACT-R Model of the Evolution of Strategy Use and Problem Difficulty.
  • Lebiere, C., Anderson, J. R., & Reder, L. M. (1994). Error modeling in the ACT-R production system. In Proceedings of the Proceedings of the Fourth International Conference on Cognitive Modeling, pp. 109-114. Mahwah, NJ: Lawrence Erlbaum Associates. [info] Sixteenth Annual Conference of the Cognitive Science Society, 555-559. Hillsdale, NJ: Erlbaum.
  • Lee, F. J. & Anderson, J. R. (2001). Does learning of a complex task have to be complex? A study in learning decomposition. Cognitive Psychology, 42(3), 267-316. [info]
  • Lovett, M. C. & Anderson, J. R. (1994). The effects of solving related proofs on memory and transfer in geometry problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(2), 366-378.
  • Singley, M. K. & Anderson, J. R. (1989). Transfer of Cognitive Skill. Cambridge, MA: Harvard University Press.
  • Vecera, S. P. & Blessing, S.B. (in press). Deductions in cognitive psychology: The role of unified theories. Skeptical Inquirer.
  • Wu, Q. & Anderson, J. R. (1991) Knowledge transfer among programming languages. In Proceedings of the 14th Annual Conference of the Cognitive Science Society, 376-381.

Cognitive Tutor Evaluation

  • Aleven, V., Koedinger, K. R., & Cross, K. (1999). Tutoring answer-explanation fosters learning with understanding. Accepted to the World Conference on Artificial Intelligence in Education.
  • 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(2).
  • Anderson, J. R. (1992). Intelligent tutoring and high school mathematics. In Proceedings of the Second International Conference on Intelligent Tutoring Systems. Montreal.
  • Anderson, J. R. (1990). Analysis of student performance with the LISP tutor. In N. Fredericksen, R. Glaser, A. Lesgold, & M. Shaffo (Eds.), Diagnostic Monitoring of Skill and Knowledge Acquisition. Hillsdale, NJ: Erlbaum, 27-50.
  • Anderson, J. R., Conrad, F. G., & Corbett, A. T. (1989). Skill acquisition and the LISP Tutor. Cognitive Science, 13, 467-506.
  • Anderson, J. R., Corbett, A. T., Koedinger, K., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of Learning Sciences, 4, 167-207.
  • Anderson, J.R. and Corbett, A.T. (1992) Acquisition of LISP Programming Skill. In S. Chipman and A. Meyrowitz (eds.) Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning. Hingham, MA: Kluwer.
  • Corbett, A. T. & Anderson, J. R. (1992). Student modeling and mastery learning in a computer-based programming tutor. In Proceedings of the Second International Conference on Intelligent Tutoring Systems. Montreal.
  • Corbett, A. T., Koedinger, K. R., & Hadley, W. H. (2001). Cognitive Tutors: From the research classroom to all classrooms. In Goodman, P. S. (Ed.) Technology Enhanced Learning: Opportunities for Change, (pp. 235-263). Mahwah, NJ: Lawrence Erlbaum Associates.
  • Corbett, A. T. & Anderson, J. R. (1992). The LISP intelligent tutoring system: Research in skill acquisition. In J. Larkin, R. Chabay, C. Scheftic (Eds.), Computer Assisted Instruction and Intelligent Tutoring Systems: Establishing Communication and Collaboration. Hillsdale, NJ: Erlbaum.
  • Corbett, A. T. & Anderson, J. R. (1990). The effect of feedback control on learning to program with the Lisp Tutor. In Proceedings of the Twelfth Annual Conference of the Cognitive Science Society, 796-803, Cambridge, MA.
  • Corbett, A. T., Anderson, J. R., & Fincham, J. M. (1991). Menu selection vs. typing: effects on learning in an intelligent programming tutor. In Proceedings of the International Conference of the Learning Sciences, 107-112. Evanston, IL.
  • Corbett, A. T., Anderson, J. R., Carver, V. H. and Brancolini, S.A. (1994). Individual differences and predictive validity in student modeling. In A. Ram & K. Eiselt (eds) The Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum.
  • Corbett, A.T. and Anderson, J.R. (1995). Knowledge decomposition and subgoal reification in the ACT programming tutor. Artificial Intelligence and Education, 1995: The Proceedings of AI-ED 95. Charlottesville, VA: AACE.
  • Corbett, A. T., Anderson, J. R., & Patterson, E. G., (1990). Student modelling and tutoring flexibility in the LISP Intelligent Tutoring System. In C. Frasson and G. Gauthier (Eds.) Intelligent tutoring systems: At the crossroads of artificial intelligence and education. 83-106. Norwood, NJ: Ablex.
  • Corbett, A.T. and Knapp, S. (1996). Plan scaffolding: Impact on the process and product of learning. In C. Frasson, G. Gauthier, & A. Lesgold, (Eds.) Intelligent tutoring systems: Third international conference , ITS 96. New York: Springer.
  • Corbett, A.T., Anderson, J.R. and O'Brien, A. T. (1993) The predictive validity of student modeling in the ACT Programming Tutor. In P. Brna, S. Ohlsson & H. Pain (eds.) Artificial Intelligence and Education, 1993: The Proceedings of AI-ED 93. Charlottesville, VA: AACE.
  • Gluck, K. A., Lovett, M. C., & Anderson, J. R. (1997). The adaptive nature of learning from Stat Lady. In Proceedings of the 19th Annual Conference of the Cognitive Science Society, p. 931. Mahwah, NJ: Erlbaum.
  • Gluck, K. A., Lovett, M. C., Anderson, J. R., & Park, J. W. (1997). Learning about learning from Stat Lady. In Proceedings of the World Conference on Educational Multimedia and Hypermedia. Charlottesville, VA: AACE.
  • Koedinger, K. & Anderson, J. R. (1993). Reifying implicit planning in geometry: Guidelines for model-based intelligent tutoring system design. In S. P. Lajoie & S. J. Derry (Eds.) Computers as cognitive tools (pp. 15-46). Hillsdale, NJ: Erlbaum.
  • Koedinger, K. R., & Anderson, J. R. (1993). Effective use of intelligent software in high school math classrooms. In Proceedings of the 1993 Conference on Artificial Intelligence in Education. Charlottesville, VA: AACE.
  • Koedinger, K. R., & Anderson, J. R. (1998). Illustrating principled design: The early evolution of a cognitive tutor for algebra symbolization. Interactive Learning Environments, 5, 161-180.
  • Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30-43. [PDF]
  • Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1995). Intelligent tutoring goes to school in the big city. In Proceedings of the 7th World Conference on Artificial Intelligence in Education, August 16-19, 1995, Washington, DC, USA.
  • Morgan, P., & Ritter, S. (2002). An experimental study of the effects of Cognitive Tutor Alegbra I on student knowledge and attitude. Pittsburgh, PA: Carnegie Learning. [http://www.carnegielearning.com/web_docs/morgan_ritter_2002.pdf]
  • Ritter, S. (1998). The Authoring Assistant. In Goettl, B. P, Halff, H. M., Redfield, C. L. and Shute, V. J. (Eds). Intelligent Tutoring Systems (pp. 126-135). Berlin: Springer-Verlag.
  • Ritter, S., Anderson, J., Cytrynowicz, M., and Medvedeva, O. (1998) Authoring Content in the PAT Algebra Tutor. Journal of Interactive Media in Education, 98 (9) [http://www-jime.open.ac.uk/98/9].
  • Ritter, S. & Anderson, J. R. (1995). Calculation and strategy in the equation solving tutor. In Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society (pp. 413-418). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Sarkis, H. (2004). Cognitive Tutor Algebra 1 Program Evaluation: Miami-Dade County Public Schools. Lighthouse Point, FL: The Reliability Group. [http://www.carnegielearning.com/web_docs/sarkis_2004.pdf
  • Schofield, J.W., Evans-Rhodes, D. and Huber, B.R. (1990). Artificial intelligence in the classroom: The impact of a computer-based tutor on teachers and students. Social Science Computer Review, 8:1, 24-41.
  • Schooler, L. J. & Anderson, J. R. (1990). The disruptive potential of immediate feedback. In Proceedings of the Twelfth Annual Conference of the Cognitive Science Society, 702-708, Cambridge, MA.
  • Shapiro, L.J., Sueker, E., & Hadley, W. (1998). Quantitative Literacy for Algebra Using Cognitive Tutoring Technology. Published in the Proceedings of the International Conference for Technology in Collegiate Mathematics. New York: AWL
  • Shapiro, L.J., Sueker, E. & Koedinger, K. (1998). Quantitative Literacy Development Algebra. Presented at the Annual Meeting of the American Educational Research Association, San Diego, CA. Wertheimer, R. (1990). The geometry proof tutor: An "intelligent" computer-based tutor in the classroom. Mathematics Teacher, April, 308-317.

Cognitive Tutor Implementation

  • Anderson, J. R. (1988). The expert module. In M. Polson & J. Richardson (Eds.), Handbook of Intelligent Training Systems.
  • Anderson, J. R. (1984). Cognitive psychology and intelligent tutoring. In Proceedings of the Sixth Annual Cognitive
  • Anderson, J. R., Corbett, A., Fincham, J., Hoffman, D., & Pelletier, R. (1992). General principles for an intelligent tutoring architecture. In V. Shute and W. Regian (Eds.), Cognitive Approaches to Automated Instruction, (pp. 81-106). Hillsdale, NJ: Erlbaum. Hillsdale, NJ: Erlbaum, 21-53. Science Meetings, 37-43.
  • Anderson, J. R. & Pelletier, R. (1991). A development system for model-tracing tutors. In Proceedings of the International Conference of the Learning Sciences, 1-8. Evanston, IL.
  • Anderson, J. R., & Reiser, B. J. (1985). The LISP tutor. Byte, 10, 159-175.
  • Blessing, S.B. (1997). A Programming by Demonstration Authoring Tool for Model-Tracing Tutors. The International Journal for Artificial Intelligence in Education., 8, 233261.
  • Blessing, S. B. (1995). ITS authoring tools: The next generation. In J. Greer (Ed.), Proceedings of AI-ED 95-7th World Conference on Artificial Intelligence and Education (p. 567). Charlottesville, VA: Association for the Advancement of Computing in Education.
  • Brusilovsky, P., Ritter, S. & Schwarz, E. (1997). Distributed intelligent tutoring on the web. In Proceedings of the World Conference on Artificial Intelligence in Education, Kobe, Japan. IOS Press.
  • Corbett, A. T. and Anderson, J. R. (1995). Knowledge decomposition and subgoal reification in the ACT programming tutor. Artificial Intelligence and Education, 1995: The Proceedings of AI-ED 95. Charlottesville, VA: AACE.
  • Corbett, A. T. & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 253-278.
  • Corbett, A. T., Koedinger, K. R., & Anderson, J. R. (1997). Intelligent tutoring systems (Chapter 37). M. G. Helander, T. K. Landauer, & P. Prabhu, (Eds.) Handbook of Human-Computer Interaction, 2nd edition. Amsterdam, The Netherlands: Elsevier Science.
  • Corbett, A.T. and Anderson, J. R. (1993) Student modeling in an intelligent programming tutor. In E. Lemut, B. du Boulay & G. Dettori (Eds.) Cognitive models and intelligent environments for learning programming. New York: Springer-Chabay, and C. Scheftic (Eds.), Computer Assisted Instruction and Intelligent Tutoring Systems: Establishing Communication and Collaboration. Hillsdale, NJ: Erlbaum.
  • Corbett, A. T. & Anderson, J. R. (1992). Student modeling and mastery learning in a computer-based programming tutor. In Proceedings of the Second International Conference on Intelligent Tutoring Systems. Montreal.
  • Corbett, A. T. & Anderson, J. R. (1992). The LISP intelligent tutoring system: Research in skill acquisition. In J. Larkin, R. Verlag.
  • Corbett, A. T., & Anderson, J. R., (1989). Feedback timing and student control in the LISP Intelligent Tutoring System. Artificial Intelligence and Education, 64-72.
  • Corbett, A. T., Anderson, J. R., Carver, V. H. & Brancolini, S. A. (1994). Individual differences and predictive validity in student modeling. In A. Ram & K. Eiselt (Eds.) The Proceedings of the Sixteenth Annual Conference of the Cognitive
  • Corbett, A.T., Anderson, J.R. and O'Brien, A.T. (1995). Student modeling in the ACT Programming Tutor. In P. Nichols, S.
  • Corbett, A. T., Anderson, J. R., & Patterson, E. J. (1988). Problem compilation and tutoring flexibility in the LISP Tutor. Science Society. Hillsdale, NJ: Lawrence Erlbaum.
  • Chipman and B. Brennan (eds.) Cognitively Diagnostic Assessment. (19-41). Hillsdale, NJ: Erlbaum. Intelligent Tutoring Systems, 423-429. Corbett, A.T. and Bhatnagar, A. (1997). Student modeling in the ACT Programming Tutor: Adjusting a procedural learning model with declarative knowledge. Proceedings of the Sixth International Conference on User Modeling.
  • Heffernan, N. & Koedinger, K.R. (2002). The Design and Formative Analysis of a Dialog-Based Tutor. Sciences et Techniques Educatives, 9(1-2), 11-35.
  • Koedinger, K. R., & Anderson, J. R. (1998). Illustrating principled design: The early evolution of a cognitive tutor for Intelligent educational systems on the World-Wide Web, Kobe, Japan.
  • Ritter, S. (1997). Pat Online: A Model-tracing tutor on the World-wide Web. In Proceedings of the Workshop on
  • Ritter, S. (1997). Communication, cooperation and competition among multiple tutor agents. In Proceedings of the World Conference on Artificial Intelligence in Education, Kobe, Japan. IOS Press.
  • Ritter, S., Anderson, J. R., Cytrynowicz, M., & Medvedeva, O. (1998) Authoring Content in the PAT Algebra Tutor. Journal of Interactive Media in Education, 98 (9). [HTML] [info]
  • Ritter, S. and Blessing, S. B. (1998). Authoring tools for component-based learning environments. Journal of the Learning Sciences, 7(1), 107-131.
  • Ritter, S. & Blessing, S. B. (1996). A programming-by-demonstration tool for retargeting instructional systems. In New York: Springer-Verlag Wein. algebra symbolization. Interactive Learning Environments, 5, 161-180. Proceedings of the International Conference on the Learning Sciences. Charlottesville, VA: Association for the Advancement of Computing in Education.
  • Ritter, S. & Blessing, S. B. (1995). Controlling content: Towards a domain-general authoring framework for intelligent tutors. In N. Major, T. Murray, and C. Bloom (Eds.), Workshop on Authoring Shells for Intelligent Tutoring Systems (pp. 78-81). Charlottesville, VA: Association for the Advancement of Computing in Education.
  • Ritter, S., Brusilovsky, P. and Medvedeva, O. (1998). Creating more versatile learning environments with a componentbased architecture. In Goettl, B. P, Halff, H. M., Redfield, C. L. and Shute, V. J. (Eds). Intelligent Tutoring Systems (pp. 126-135). Berlin: Springer-Verlag.
  • Ritter, S. & Koedinger, K. R. (1996). An architecture for plug-in tutor agents. Journal of Artificial Intelligence in Education, 7, 315-347.
  • Ritter, S. & Koedinger, K. R. (1995). Towards Lightweight Tutoring Agents. In Proceedings of the World Conference on Artificial Intelligence in Education. Charlottesville, VA: Association for the Advancement of Computing in Education.

Mathematics Education

  • Anderson, J. R. (1992). Intelligent tutoring and high school mathematics. In Proceedings of the Second International Conference on Intelligent Tutoring Systems. Montreal.
  • Anderson, J. R. (1983). A general learning theory and its application to the acquisition of proof skills in geometry. In R. Michalski, J. Carbonell, and T. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach. Palo Alto, CA: Tioga Publishing.
  • Anderson, J. R. (1981). Tuning of search of the problem space for geometry proofs. In Proceedings of IJCAI-81, 97-103.
  • Anderson, J. R., Boyle, C. F., & Yost, G. (1985). The geometry tutor. In Proceedings of IJCAI, 1-7.
  • Anderson, J. R., Greeno, J. G., Reder, L. M., & Simon, H. A. (2000). Perspectives on learning, thinking, and activity. Educational Researcher, 29, 11-13. [info]
  • Anderson, J. R., Reder, L. M. & Simon, H. (1998). Radical constructivism and cognitive psychology. In D. Ravitch (Ed.) Brookings papers on education policy 1998. Washington, DC: Brookings Institute Press.
  • Anderson, J. R. & Schunn, C. D. (2000). Implications of the ACT-R learning theory: No magic bullets. In R. Glaser, (Ed.), Advances in instructional psychology: Educational design and cognitive science (Volume 5), pp. 1-34. Mahwah, NJ: Lawrence Erlbaum Associates. [info]
  • Anderson, J. R., Simon, H. A., & Reder, L. M. (1996). Situated learning and education. Educational Researcher, 25, 5-11.
  • Anderson, J. R., Simon, H. A., & Reder, L. M. (1997). Rejoiner: Situative versus cognitive perspectives: Form versus substance. Educational Researcher, 26, 18-21.
  • Anderson, J. R., Simon, H. A., & Reder, L. M. (non-published). Applications and misapplications of cognitive psychology to mathematics education. [HTML] [info]
  • Haverty, L. A., Koedinger, K. R., Klahr, D., & Alibali, M. W. (2000). Solving induction problems in mathematics: Not-sotrivial pursuit. Cognitive Science, 24, 249-298.
  • Heffernan, N. & Koedinger, K. R. (1998). A developmental model for algebra symbolization: The results of a difficulty factors assessment. In Proceedings of the Twentieth Annual Conference of the Cognitive Science Society, (pp. 484-489). Hillsdale,NJ: Erlbaum.
  • Heffernan, N. & Koedinger, K. R. (1997). The composition effect in symbolizing: The role of symbol production vs. text comprehension. In Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, (pp. 307-312.. Hillsdale, NJ: Erlbaum.) (Marr prize winner for best student paper.)
  • Koedinger, K. R. & Alibali, M. W. (1999). A developmental model of algebra problem solving: Trade-offs between grounded and abstract representations. Paper prepared for the annual meeting of the American Educational Research Association, Montreal, Canada.
  • Koedinger, K. & Anderson, J. R. (1991) Interaction of deductive and inductive reasoning strategies in geometry novices. In Proceedings of the 14th Annual Conference of the Cognitive Science Society, 780-784.
  • Koedinger, K. R., & Anderson, J. R. (1990). Abstract planning and perceptual chunks: Elements of expertise in geometry. Cognitive Science, 14, 511-550.
  • Koedinger, K. R. & Nathan, M. J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. The Journal of the Learning Sciences, 13 (2).
  • Lovett, M. C. & Anderson, J. R. (1994). The effects of solving related proofs on memory and transfer in geometry problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(2), 366-378.
  • Singley, M. K., Anderson, J. R., & Gevins, J. S. (1991). Promoting abstract strategies in algebra word problem solving. In Proceedings of the International Conference of the Learning Sciences, 398-404. Evanston, IL.
  • Singley, M. K., Anderson, J. R., Gevins, J. S., & Hoffman, D. (1989). The algebra word problem tutor. Artificial Intelligence and Education, 267-275.

Theoretical Papers

  • Anderson, J. R. (2002). Spanning seven orders of magnitude: A challenge for cognitive modeling. Cognitive Science, 26. [info]
  • Anderson, J. R. (1991). Is human cognition adaptive? Behavioral and Brain Sciences, 14, 471-484.
  • Anderson, J. R. (1987). Production systems, learning, and tutoring. In D. Klahr, P. Langley, & R. Neches (Eds.), Selfmodifying production systems: Models of learning and development. Bradford Books/MIT, Cambridge, MA.
  • Anderson, J. R., Boyle, C. F., Corbett, A., and Lewis, M. W. (1990) Cognitive modelling and intelligent tutoring. Artificial Intelligence, 42, 7-49.
  • Anderson, J. R., Boyle, C. F., Farrell, R., & Reiser, B. J. (1987). Cognitive principles in the design of computer tutors. [info] P. Morris (Ed.), Modelling Cognition, Wiley.
  • Anderson, J. R., Boyle, C. F., Farrell, R., & Reiser, B. J. (1984). Cognitive principles in the design of computer tutors. In Proceedings of the Sixth Annual Cognitive Science Meetings, 2-10.
  • Anderson, J. R., Boyle, C. F., & Reiser, B. J. (1985). Intelligent tutoring systems. Science, 228, 456-467.
  • Anderson, J. R. & Gluck, K. (2001). What role do cognitive architectures play in intelligent tutoring systems? In D. Klahr & S. M. Carver (Eds.) Cognition & Instruction: Twenty-five years of progress, 227-262. Erlbaum. [info]
  • Anderson, J. R., Simon, H. A., & Reder, L. M. (1996). Situated learning and education. Educational Researcher, 25, 5-11.
  • Anderson, J. R., John, B. E., Just, M. A., Carpenter, P. A., Kieras, D. E., & Meyer, D. E. (1995). Production system models of complex cognition. In Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society (pp. 9-12). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Koedinger, K. & Anderson, J. R. (1993). Reifying implicit planning in geometry: Guidelines for model-based intelligent tutoring system design. In S. P. Lajoie & S. J. Derry (Eds.) Computers as cognitive tools (pp. 15-46). Hillsdale, NJ: Erlbaum.
  • Koedinger, K. R. (2001). Cognitive tutors as modeling tool and instructional model. In Forbus, K. D. & Feltovich, P. J. (Eds.) Smart Machines in Education: The Coming Revolution in Educational Technology, (pp. 145-168). Menlo Park, CA: AAAI/MIT Press.
  • Koedinger, K. R. (2002). Toward evidence for instructional design principles: Examples from Cognitive Tutor Math 6. Invited paper in Proceedings of PME-NA XXXIII (the North American Chapter of the International Group for the Psychology of Mathematics Education).