Quantitative Impact of a Cognitive Modeling Intelligent Tutoring System on Student Performance in Balancing Chemical Equations

2002 ◽  
Vol 7 (6) ◽  
pp. 379-383 ◽  
Author(s):  
Mary Beth Walsh ◽  
Connie M. Moss ◽  
Benny G. Johnson ◽  
Dale A. Holder ◽  
Jeffry D. Madura
Keyword(s):  
Student Performance ◽  
Cognitive Modeling ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Chemical Equations ◽  
Quantitative Impact

User Evaluation of the Lisp Intelligent Tutoring System

1986 ◽  
Vol 30 (2) ◽  
pp. 182-186 ◽  
Author(s):  
Douglas G. Hoecker ◽  
Glenn S. Elias
Keyword(s):  
Student Performance ◽  
Error Detection ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Computer Usage ◽  
Rule Base ◽  
Tutoring System ◽  
Accounting Data ◽  
Industrial Environment ◽  
Final Exams

In a cooperative arrangement between Westinghouse and Carnegie-Mellon University, a test version of CMU's LISP Intelligent Tutoring System (LISPITS) was installed on a Westinghouse VAX 11/785 that could be accessed by engineering personnel from company sites anywhere in the country. The object of this research was to evaluate LISPITS's performance in a more industrial environment than heretofore attempted. More specific research questions concerned (a) dialog structure, (b) computer resource requirements for large numbers of students, (c) rule–base applicability to students of different backgrounds, and (d) LISPITS's effectiveness as measured by student performance. Four classes of data were collected: (1) computer usage (accounting data) (2) 34–item questionnaires, (3) mid–term and final exams, (4) computer–readable files of activity in both the exercise and coding windows. Results suggest (a) overall, this group's experience with LISPITS was a positive one, and that basically this technology works in an industrial environment; (b) dialog management, while adequate, could be further optimized on the current 80–column x 24–line display; (c) dialog could be improved even more greatly with a more advanced display (e.g., more and larger windows, high resolution, bit-napped text and highlights) (d) two aspects of the interaction appear to have salient impact on learning and user acceptance for students with professional engineering experience: (1) the tutor's flexibility in dealing with potentially–valid student solutions, and (2) the level of analysis that governs error detection, diagnosis, and intervention strategies.

Student Performance Evaluation for a Simulation Based Intelligent Expert Tutoring System

1988 ◽  
Vol 32 (18) ◽  
pp. 1212-1216
Author(s):  
C. H. Lee ◽  
J. E. Biegel ◽  
C. M. Dixon
Keyword(s):  
Performance Assessment ◽  
Student Performance ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Real Life ◽  
Training System ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Simulation Based ◽  
Student’S Performance

Intelligent tutoring systems offer an exciting new way to train people in areas of complex domains. A simulation-based training system provides the student with the opportunity to manipulate a system without the consequences of real life mistakes. The intelligence required in the tutoring system is focused on the tutor's ability to teach the student efficient, strategic responses. This tutoring demands that the tutor is aware of the student's current ability, specific fault areas, and preferred method of tutoring. Instructional decisions are made by assessing the student's performance. The utility of an intelligent tutoring system depends on its capacity to evaluate the student's performance. Performance assessment then has significant impact on the employment of such a system. The parameters used for performance assessment of a complex task depend on the objective of the tutoring system. We present a description of a generic intelligent tutoring system which will remove the human instructor from the training loop.

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