Effective Feedback Content for Tutoring Complex Skills

Feedback during learning is critical for evaluating new skills. Computer-based tutoring systems have the potential to detect errors and to guide students by providing informative feedback, but few studies have evaluated the real impact of different types of feedback. This article presents results of such a study using the Geometry Tutor for building geometry proofs. It was found that feedback about the goal structure of geometry problems led to better performance than feedback about the reasons for error or than simply being told that an error had occurred. This goal feedback allows students to correct the incorrect action more often than other types of feedback. Also, the goal feedback group continued to deal advantageously with problems when the feedback was subsequently removed. A simulation model, based on Anderson's (1983) ACT* theory and an analogical learning system, presents a preliminary model of the effects of these different feedback types. The model indicates that the advantage of goal-directed feedback is a reflection of its immediate application to the problem, whereas feedback about reasons for the error does not provide any direction to the correct error. According to the model, the feedback allows the student to construct a correct representation of the goal tree involved in various types of proofs more readily than feedback that is not immediately relevant to the current problem.

[1]  John Seely Brown,et al.  An Investigation of Computer Coaching for Informal Learning Activities. , 1978 .

[2]  H. Mandl,et al.  Improvement of the acquisition of knowledge by informing feedback , 1988 .

[3]  L. Resnick,et al.  Psychology of Mathematics for Instruction , 1981 .

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

[5]  John M. Carroll,et al.  The adventure of getting to know a computer , 1982, Computer.

[6]  Allan Collins,et al.  Misconceptions in student's understanding , 1979 .

[7]  E. R. Williams.  An investigation of senior high school students understanding of the nature of mathematical proof , 1979 .

[8]  Albert T. Corbett,et al.  Intelligent Tutoring Systems , 1985, Science.

[9]  J. S. Brown,et al.  Pedagogical, natural language, and knowledge engineering techniques in SOPHIE-I, II and III , 1982 .

[10]  John Seely Brown,et al.  Process versus Product: A Perspective on Tools for Communal and Informal Electronic Learning , 1985 .

[11]  Stephen M. Casner,et al.  ANALYSIS-BASED LEARNING IN HUMAN-COMPUTER INTERACTION , 1987 .

[12]  J. McKendree,et al.  Advising roles of a computer consultant , 1986, CHI '86.

[13]  Clayton Lewis,et al.  Why and How to Learn Why: Analysis-Based Generalization of Procedures , 1988, Cogn. Sci..

[14]  C. Solomon.  Computer Environments for Children: A Reflection on Theories of Learning and Education , 1986 .

[15]  Ira P. Goldstein,et al.  The genetic graph: a representation for the evolution of procedural knowledge , 1979 .

[16]  William J. Clancey,et al.  Tutoring rules for guiding a case method dialogue , 1979 .

[17]  James L. Alty,et al.  Face-to-face guidance of university computer users—II. Characterizing advisory interactions , 1980 .

[18]  Etienne Wenger,et al.  Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge , 1987 .

[19]  John M. Carroll,et al.  Interface design issues for advice-giving expert systems , 1987, CACM.

[20]  John M. Carroll,et al.  Prompting, feedback and error correction in the design of a scenario machine , 1985, CHI '85.

[21]  Albert T. Corbett,et al.  Intelligent Tutoring Systems , 1985, Science.

[22]  John R. Anderson,et al.  Discrimination of operator schemata in problem solving: Learning from examples , 1985, Cognitive Psychology.

[23]  Mikhail V. Matz.  A process model for high school algebra errors , 1982 .

[24]  Allen Newell,et al.  The psychology of human-computer interaction , 1983 .

[25]  P. Pirolli,et al.  The role of learning from examples in the acquisition of recursive programming skills. , 1985 .

[26]  John R. Anderson,et al.  Learning to Program in LISP , 1984, Cogn. Sci..

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

[28]  John R. Anderson,et al.  The Geometry Tutor , 1985, IJCAI.

[29]  John M. Carroll,et al.  Impact of feedback content in initial learning of an office system , 1987 .

[30]  John R. Anderson,et al.  Learning to Program in LISP , 1984, Cogn. Sci..

[31]  John R. Anderson,et al.  Effect of practice on knowledge and use of basic Lisp , 1987 .

[32]  Seymour Papert,et al.  Mindstorms: Children, Computers, and Powerful Ideas , 1981 .

[33]  Jaime R. Carbonell,et al.  AI in CAI : An artificial intelligence approach to computer-assisted instruction , 1970 .

[34]  Judith D. Wilson,et al.  Artificial Intelligence and Tutoring Systems , 1990 .

[35]  John R. Anderson,et al.  Dynamic Student Modelling in an Intelligent Tutor for LISP Programming , 1985, IJCAI.

[36]  Cynthia Solomon.  Computer Environments for Children: A Reflection on Theories of Learning and Education , 1986 .

[37]  Tim O'Shea,et al.  A self-improving quadratic tutor , 1979 .

[38]  Roy D. Pea,et al.  Logo Programming and the Development of Planning Skills. Technical Report No. 16. , 1984 .

[39]  Jean-Marc Robert.  LEARNING A COMPUTER SYSTEM BY UNASSISTED EXPLORATION , 1987 .