Science Watch Problem Solving and Learning

Newell and Simon (1972) provided a framework for understanding problem solving that can provide the needed bridge between learning and performance. Their analysis of means-ends problem solving can be viewed as a general characterization of the stmcture of human cognition. However. this framework needs to be elaborated with a strength concept to account for variability in problemsolving behavior and improvement in problem-solving skill with practice. The ACT* theory (Anderson. 1983) is such an elaborated theory that can account for many of the results about the acquisition of problem-solving skills. Its central concept is the production rule, which plays an analogous role to the stimulus-response bond in earlier learning theories. The theory has provided a basis for constructing intelligent computer-based tutoring systems for the instruction of academic problem-solving skills.

[1]  John R. Anderson,et al.  Skill Acquisition : Compilation of Weak-Method Problem Solutions , 2004 .

[2]  A. Newell.  Reasoning, problem solving, and decision processes: the problem space as a fundamental category , 1993 .

[3]  John R. Anderson,et al.  Intelligent Tutoring and High School Mathematics , 1992, Intelligent Tutoring Systems.

[4]  S. Kay.  On the Nature of Expertise. , 1992 .

[5]  John R. Anderson,et al.  Reflections of the Environment in Memory Form of the Memory Functions , 2022 .

[6]  Craig A. Kaplan,et al.  In search of insight , 1990, Cognitive Psychology.

[7]  John R. Anderson,et al.  Cognitive Modeling and Intelligent Tutoring , 1990, Artif. Intell..

[8]  Stephen J. Payne,et al.  Algebra Mal-Rules and Cognitive Accounts of Error , 1990, Cogn. Sci..

[9]  John R. Anderson,et al.  Use of analogy in a production system architecture , 1989 .

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

[11]  K. VanLehn.  Problem solving and cognitive skill acquisition , 1989 .

[12]  Herbert A. Simon,et al.  THE MIND'S EYE IN CHESS , 1988 .

[13]  John R. Anderson,et al.  Novice LISP Errors: Undetected Losses of Information from Working Memory , 1985, Hum. Comput. Interact..

[14]  Peter L. Pirolli,et al.  Problem solving by analogy and skill acquisition in the domain of programming , 1985 .

[15]  Hasler Rudy Hayes,et al.  Three problems in teaching general skills , 1985 .

[16]  B. Ross.  Remindings and their effects in learning a cognitive skill , 1984, Cognitive Psychology.

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

[18]  Allen and Rosenbloom Paul S. Newell,et al.  Mechanisms of Skill Acquisition and the Law of Practice , 1993 .

[19]  Paul J. Feltovich,et al.  Categorization and Representation of Physics Problems by Experts and Novices , 1981, Cogn. Sci..

[20]  Kurt VanLehn,et al.  Repair Theory: A Generative Theory of Bugs in Procedural Skills , 1980, Cogn. Sci..

[21]  Robin Jeffries,et al.  The Processes Involved in Designing Software. , 1980 .

[22]  R. Jeffries,et al.  A process model for Missionaries-Cannibals and other river-crossing problems , 1977, Cognitive Psychology.

[23]  N. Charness.  Memory for Chess Positions: Resistance to Interference. , 1976 .

[24]  Michael E Atwood,et al.  A process model for water jug problems , 1976, Cognitive Psychology.

[25]  H. Simon,et al.  The functional equivalence of problem solving skills , 1975, Cognitive Psychology.

[26]  Nils J. Nilsson,et al.  Problem-solving methods in artificial intelligence , 1971, McGraw-Hill computer science series.

[27]  CYRIL BURT.  A Theory of Human Knowledge , 1970, Nature.

[28]  M. Scheerer,et al.  Problem Solving , 1967, Nature.

[29]  C. Burt.  THE PSYCHOLOGY OF LEARNING , 1958 .

[30]  Sher ry Folsom-Meek,et al.  Human Performance , 2020, Nature.

[31]  F. W. Irwin.  Purposive Behavior in Animals and Men , 1932, The Psychological Clinic.

[32]  W. Köhler.  The Mentality of Apes. , 2018, Nature.

[33]  E. Thorndike.  “Animal Intelligence” , 1898, Nature.