Exploring the Assistance Dilemma in Experiments with Cognitive Tutors

Intelligent tutoring systems are highly interactive learning environments that have been shown to improve upon typical classroom instruction. Cognitive Tutors are a type of intelligent tutor based on cognitive psychology theory of problem solving and learning. Cognitive Tutors provide a rich problem-solving environment with tutorial guidance in the form of step-by-step feedback, specific messages in response to common errors, and on-demand instructional hints. They also select problems based on individual student performance. The learning benefits of these forms of interactivity are supported, to varying extents, by a growing number of results from experimental studies. As Cognitive Tutors have matured and are being applied in new subject-matter areas, they have been used as a research platform and, particularly, to explore interactive methods to support metacognition. We review experiments with Cognitive Tutors that have compared different forms of interactivity and we reinterpret their results as partial answers to the general question: How should learning environments balance information or assistance giving and withholding to achieve optimal student learning? How best to achieve this balance remains a fundamental open problem in instructional science. We call this problem the “assistance dilemma” and emphasize the need for further science to yield specific conditions and parameters that indicate when and to what extent to use information giving versus information withholding forms of interaction.

[1]  N. J. Slamecka,et al.  The Generation Effect: Delineation of a Phenomenon , 1978 .

[2]  Beyond the Purely Cognitive: Belief Systems, Social Cognitions, and Metacognitions As Driving Forces in Intellectual Performance , 1983, Cogn. Sci..

[3]  B. Bloom.  The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring , 1984 .

[4]  J. Sweller,et al.  The Use of Worked Examples as a Substitute for Problem Solving in Learning Algebra , 1985 .

[5]  H. Simon,et al.  Learning Mathematics From Examples and by Doing , 1987 .

[6]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[7]  Matthew W. Lewis,et al.  Self-Explonations: How Students Study and Use Examples in Learning to Solve Problems , 1989, Cogn. Sci..

[8]  John R. Anderson,et al.  Skill Acquisition and the LISP Tutor , 1989, Cogn. Sci..

[9]  Jean McKendree,et al.  Effective Feedback Content for Tutoring Complex Skills , 1990, Hum. Comput. Interact..

[10]  J. Sweller,et al.  Structuring Effective Worked Examples , 1990 .

[11]  Kurt VanLehn,et al.  A model of the self-explanation effect. , 1992 .

[12]  J. Gregory Trafton,et al.  Effective Tutoring Techniques: A Comparison of Human Tutors and Intelligent Tutoring Systems , 1992 .

[13]  R. Schmidt,et al.  New Conceptualizations of Practice: Common Principles in Three Paradigms Suggest New Concepts for Training , 1992 .

[14]  Susanne P. Lajoie,et al.  Computers As Cognitive Tools , 2020 .

[15]  John R. Anderson,et al.  Rules of the Mind , 1993 .

[16]  F. Paas,et al.  Variability of Worked Examples and Transfer of Geometrical Problem-Solving Skills: A Cognitive-Load Approach , 1994 .

[17]  Michelene T. H. Chi,et al.  Eliciting Self-Explanations Improves Understanding , 1994, Cogn. Sci..

[18]  John R. Anderson,et al.  Cognitive Tutors: Lessons Learned , 1995 .

[19]  S B Dowd,et al.  Computer-based instruction. , 1995, Radiologic technology.

[20]  D. Allen,et al.  The power of problem‐based learning in teaching introductory science courses , 1996 .

[21]  A. Kluger,et al.  The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. , 1996 .

[22]  Stellan Ohlsson,et al.  Learning from Performance Errors. , 1996 .

[23]  Ray Eberts,et al.  Chapter 36 – Computer-Based Instruction , 1997 .

[24]  T. Landauer,et al.  Handbook of Human-Computer Interaction , 1997 .

[25]  Mary A. Mark,et al.  An Interview Reflection on “Intelligent Tutoring Goes to School in the Big City” , 2015, International Journal of Artificial Intelligence in Education.

[26]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .

[27]  Daniel L. Schwartz,et al.  A time for telling , 1998 .

[28]  Mitchell J. Nathan.  Knowledge and Situational Feedback in a Learning Environment for Algebra Story Problem Solving , 1998, Interact. Learn. Environ..

[29]  H. Mandl,et al.  Learning from Worked-Out Examples: The Effects of Example Variability and Elicited Self-Explanations , 1998, Contemporary educational psychology.

[30]  Herbert A. Simon,et al.  Radical constructivism and cognitive psychology , 1998 .

[31]  J. Frederiksen,et al.  Inquiry, Modeling, and Metacognition: Making Science Accessible to All Students , 1998 .

[32]  Vincent Aleven,et al.  Limitations of Student Control: Do Students Know When They Need Help? , 2000, Intelligent Tutoring Systems.

[33]  Vincent Aleven,et al.  The Need for Tutorial Dialog to Support Self-Explanation , 2000 .

[34]  S. Derry,et al.  Learning from Examples: Instructional Principles from the Worked Examples Research , 2000 .

[35]  Albert T. Corbett,et al.  Cognitive Computer Tutors: Solving the Two-Sigma Problem , 2001, User Modeling.

[36]  Joseph Krajcik,et al.  Learning Science Content in a Project-Based Environment , 2001 .

[37]  Deborah Allen,et al.  The power of problem-based learning : a practical "how to" for teaching undergraduate courses in any discipline , 2001 .

[38]  Joseph Krajcik,et al.  Portable Technologies: Science Learning in Context. Innovations in Science Education and Technology. , 2001 .

[39]  Slava Kalyuga,et al.  When problem solving is superior to studying worked examples. , 2001 .

[40]  Steven Ritter,et al.  An experimental study of the effects of Cognitive Tutor® Algebra I on student knowledge and attitude , 2002 .

[41]  Vincent Aleven,et al.  An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor , 2002, Cogn. Sci..

[42]  Diane Ravitch,et al.  Brookings Papers on Education Policy: 1998 , 2002 .

[43]  A. Renkl.  Worked-out examples: instructional explanations support learning by self- explanations , 2002 .

[44]  R. Siegler.  Microgenetic Studies of Self-Explanation , 2002 .

[45]  Kenneth R. Koedinger.  Toward Evidence for Instructional Design Principles: Examples from Cognitive Tutor Math 6 , 2002 .

[46]  N. Granott,et al.  Microdevelopment: Microdevelopment: A process-oriented perspective for studying development and learning , 2002 .

[47]  R. Atkinson,et al.  Transitioning From Studying Examples to Solving Problems: Effects of Self-Explanation Prompts and Fading Worked-Out Steps. , 2003 .

[48]  Kenneth R. Koedinger,et al.  Recasting the feedback debate: benefits of tutoring error detection and correction skills , 2003 .

[49]  V. Aleven,et al.  Help Seeking and Help Design in Interactive Learning Environments , 2003 .

[50]  Ryan Shaun Joazeiro de Baker,et al.  Off-task behavior in the cognitive tutor classroom: when students "game the system" , 2004, CHI.

[51]  Cornelia S. Große,et al.  How Fading Worked Solution Steps Works – A Cognitive Load Perspective , 2004 .

[52]  Milena K. Nigam,et al.  The Equivalence of Learning Paths in Early Science Instruction: Effects of Direct Instruction and Discovery Learning , 2022 .

[53]  Gary S Plano,et al.  The Effects of the Cognitive Tutor Algebra on Student Attitudes and Achievement In a 9th Grade Algebra Course , 2004 .

[54]  K. Koedinger,et al.  Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring , 2005 .

[55]  R. Sawyer.  The Cambridge Handbook of the Learning Sciences: Introduction , 2014 .

[56]  M. Cole,et al.  Mind in society: The development of higher psychological processes. L. S. Vygotsky. , 1978 .

[57]  John R. Anderson,et al.  Knowledge tracing: Modeling the acquisition of procedural knowledge , 2005, User Modeling and User-Adapted Interaction.

[58]  Rebecca S. Crowley,et al.  An ITS for medical classification problem-solving: Effects of tutoring and representations , 2005, AIED.

[59]  Kurt VanLehn,et al.  The Andes Physics Tutoring System: Lessons Learned , 2005, Int. J. Artif. Intell. Educ..

[60]  Vincent Aleven,et al.  Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor , 2006, Int. J. Artif. Intell. Educ..

[61]  Kenneth R. Koedinger,et al.  Studying the Effects of Personalized Language and Worked Examples in the Context of a Web-Based Intelligent Tutor , 2006, Intelligent Tutoring Systems.

[62]  Mitsuru Ikeda,et al.  Proceedings of the 8th international conference on Intelligent Tutoring Systems , 2006 .

[63]  Antonija Mitrovic,et al.  Constraint-based knowledge representation for individualized instruction , 2006, Comput. Sci. Inf. Syst..

[64]  Ryan Shaun Joazeiro de Baker,et al.  Adapting to When Students Game an Intelligent Tutoring System , 2006, Intelligent Tutoring Systems.

[65]  Kurt VanLehn,et al.  The Behavior of Tutoring Systems , 2006, Int. J. Artif. Intell. Educ..

[66]  Rebecca S. Crowley,et al.  An intelligent tutoring system for visual classification problem solving , 2006, Artif. Intell. Medicine.

[67]  Vincent Aleven,et al.  The Help Tutor: Does Metacognitive Feedback Improve Students' Help-Seeking Actions, Skills and Learning? , 2006, Intelligent Tutoring Systems.

[68]  David Richard Moore,et al.  E-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning , 2006 .

[69]  Richard E. Clark,et al.  Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching , 2006 .

[70]  Philip I. Pavlik.  Timing Is in Order: Modeling Order Effects in the Learning of Information , 2007 .

[71]  Jörg Wittwer,et al.  Can tutored problem solving benefit from faded worked-out examples? , 2007 .