Intelligent Computer Aided Instruction System Research Articles

Research on Intelligent Computer Aided Instruction System

This paper first outline the present conditions of Intelligent Computer Aided Instruction, then elaborate the contents of Intelligent Computer Aided Instruction system, and finally discuss the future development direction of the Intelligent Computer Aided Instruction system.

Effect of Intelligent Instruction on Students' Mental Models and Performance

The effects of two types of Intelligent Computer Aided Instruction (ICAI) and a non-intelligent tutoring system on the development of students' mental models and on performance was explored. One of the two ICAI systems provided both principle-based and task specific advice while the other provided task specific advice only. The non-intelligent CAI provided no advice. The problems presented to students increased in difficulty as the student progressed. It was hypothesized that the mental models of students who interacted with the ICAI that provided both principle-based and task specific advice would be most similar in structure to a “gold standard” model, and that these students would also exhibit the best performance. It was also hypothesized that students who were exposed to the ICAI that provided only task specific advice would perform better, and exhibit more accurate and detailed mental models than would students who interacted with the CAI that provided no advice. Study results indicated that students in both ICAI conditions achieved a higher similarity index, fewer errors, and faster task time, than did students in the no advice group. However, there was no significant difference between the two ICAIs for any of these measures. There was a significant interaction between version and problem difficulty for task time. As problem difficulty increased, task time increased for both of the two ICAI conditions, but not for the no-advice condition. These results are discussed in terms of their implications for the predictive validity of the Pathfinder measure, and in terms of the appropriate use and design of ICAI.

Intelligent computer-aided instruction: a survey organized around system components

The issues and previous research in intelligent computer-aided instruction (ICAI) are surveyed, concentrating on the contribution of each effort to understanding the various components of ICAI systems. Comparisons are made between ICAI and CAI. Various learning scenarios are discussed including computer coaches, gaming environments, mixed initiative dialog, Socratic tutors, articulate experts, interactive simulation, and discovery learning. Various forms of knowledge representation are discussed along with relevant issues and examples. Several techniques for student modeling and diagnosis are presented, as are their respective advantages and disadvantages. Pedagogical knowledge, its role in ICAI and several examples are highlighted. The evolution of discourse-management techniques for ICAI is outlined. Techniques for the automatic generation of problems from a general base of domain knowledge are presented. The design of user interfaces for ICAI systems is briefly discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Intelligent computer-aided instruction for the coding task

This paper describes an intelligent computer-aided instruction (ICAI) system for the task of translating a high level program design language (PDL) into a programming language. It enforces the desirable separation of algorithm design and coding by focusing on coding as an independent task. A production system formalism is used to represent the rules needed to do the translation. The intelligent tutor program presents a PDL program to the learner and then uses the production rules to translate it into FORTRAN 77, saving the correct set of rules needed. It then inputs the learner's translation, and when it is not correct, it attempts to determine those rules not applied correctly. After isolating an error, it is capable of several response levels which give the learner maximum opportunity to discover her his own error. These responses involve the generation of remedial PDL programs which highlight the rule that has presented difficulty to the learner. Finally, methods of incorporating the ICAI system into a beginning Computer Science course are presented.