JAIED v8n1

The purpose of academic advising is to help a student develop educational plans. Inuniversities it is usual for faculty to perform academic advising. Recently a number ofrule-based systems were developed to help faculty and students with the advising task.Some of these systems were reviewed in our previous paper, where we posed a number ofmajor goals for constructing knowledge-based advisors. SAIKIC, our academic advisingsystem, is designed to incorporate those goals. In this paper we describe our progresswith the design of SAIKIC.

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An Expert Advisor for Vocational Guidance

JOACHIM P. HASEBROOK

Bank Academy, Oeder Weg 16-18, D-60318

Frankfurt, Germany

WOLFGANG NATHUSIUS

Medialog Corporation, Kolpingstr. 18, D-68165

Mannheim, Germany

We developed a multimedia program which combines a vocational encyclopedia and atesting facility to foster adequate career decisions. The testing facility is designed tosuggest the same careers which a given number of experts would have suggested if presentedwith the same user´s input. Our vocational database includes imprecise data, like expertratings, enabling the calculation of suggestions of career options. The most importantgroup of software users for vocational guidance are young adults who are about to leaveschool. The results of cluster analyses (n=426) showed that the interests of students werepoorly structured and were not compatible with experts´ ratings. The test facility hasbeen implemented on several CD-ROMs, in a short quiz to identify occupational fields, andin a wide range of surveys which was answered through letters. Forty-three studentsparticipated in an experiment to investigate the understanding and acceptance of theinformation provided by the system. The results showed that students were able to judgewhether careers matched their individual interests or not. Furthermore, we exploredwhether the system was able to reconstruct 38 experts´ ratings. The system showed a goodperformance in reconstructing the experts´ data&emdash;except with one academiccareer which was not described very clearly. In a recent study, we tested the influence ofthe testing facility on recall of information and individual acceptance (n=75). Acceptanceand recall about career options were clearly enhanced, when studying individualizedmaterials compared to general information.

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Toward Computational Models of Motivation: A Much Needed Foundation for SocialSciences and Education

MICHEL AUBÉ

Université de Sherbrooke

Sherbrooke, Quebec J1K 2R1, Canada

Most basic concepts about knowledge representation, such as declarative or proceduralknowledge, come from research in artificial intelligence and have percolated from therethrough other cognitive sciences to finally reach the domain of educational practice.Therein they are beginning to have a profound impact upon curriculum design, didactics,teaching strategies, and teacher education. Meanwhile, the domain of student motivation iscalling for increasing attention, but existing models remain essentially descriptive: Theyrelate the variables that are known to matter empirically, yet they offer littleexplanatory power about what activates willful behavior and goal orientation. In thispaper we advocate the need for basic computational models of motivation as new fundamentaltools for education and suggests using the design-based approach of artificialintelligence to lay down such models. Motivation includes physiological needs as well asemotional reactions, but our proposal for a model will only be concerned with emotions. Itis argued that motivation has to do with resource management, and that the particularresources that concern emotions are the ones that are obtained from others, through theirhaving been committed to provide them. Thus viewed, emotions are essentially of a socialnature and have to do with regulating and managing commitments that bind individualstogether in action, communication, and cooperation. Preliminary specifications for acomputational model of emotions are formulated, and consequences for current theories ofemotions and of education are formulated in conclusion.

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Analogy, Logic Programming, and Metacognition

ANTONIO M. LOPEZ, JR.

Mathematics and Computer Science

Loyola University, Campus Box 51

New Orleans, LA 70118-6195, USA

This paper presents an analogy microworld used in an artificial intelligence (AI)course to stimulate student metacognition. Students write programs in a logic programmingenvironment that can answer queries about analogies in the microworld. In developing theseprograms, the students prepare interim reports that explain their thinking. With thesereports the instructor can guide and clarify the student’s thinking. In an AI course,the cognitive effects of programming computers to do intelligent things greatly depend onthe mindful engagement of the students on these tasks.

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Using Students’ Knowledge to Generate Individual Feedback: Concept for anIntelligent Educational System on Logistics

DIETRICH ZIEMS AND GABY NEUMANN

Department of Logistics and Materials Handling Engineering

Otto-von-Guericke-University of Magdeburg

Postfach 4120, D-39016 Magdeburg, Germany

Engineering education mainly focuses on enabling the student to deal independently withcomplex and complicated problem-solving processes in a varying manner. This difficultlearning process is to be supported especially for self-studies by context-sensitivefeedback as well as methods and rules for analyzing and evaluating solutions suggested bythe student. In the following paper we will discuss ideas for a methods kit forinteractive exercises with varying complexity and difficulty as well as for a suitablemethodology for an intelligent evaluation of solutions. We will also investigate how thequality of an existing educational system on logistics can be decisively improved using AItechniques. By embedding a rule-based diagnosis module that evaluates the student’sknowledge on the basis of a viewpoint description, the student is integrated into thedialogue with the educational system in a much more active way.

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Application of Fuzzy Logic Techniques in the BSS1 Tutoring System

KAI WARENDORF AND SU JEN TSAO

School of Applied Science, Nanyang Technological University

Nanyang Avenue, Singapore 639798

The Brilliant Scholar Series 1 (BSS1) is a tutoring system currently used by severalthousand home and school users in the learning of curricular subjects such as mathematicsand sciences. It is an AI-based tutoring system using heuristics to interact with usersand to monitor their progress. It is believed that the use of fuzzy logic techniques canimprove the performance of such tutoring systems, by introducing intelligent featureswhich can better manage the student’s learning. A general fuzzy logic engine wasdesigned and implemented to support development of intelligent features for BSS1. In orderto develop such features, the problem had to be suitably modeled and a knowledge basecreated, followed by testing and tuning with appropriate procedures.

The usefulness of such a fuzzy system depends on the engineer’s ability to modelthe problem suitably, define fuzzy variables and suitable membership functions for theirfuzzy sets, and develop a comprehensive set of rules relating input and output variables.The average engineer who may not be equipped with this knowledge is still able to design(simple) systems by just manipulating the fuzzy set functions and the rules. Internalparameters which are generally provided by the user of the engine (the expert systemdesigner) are fixed in this application by choosing widely used methods which have provedeffective and are commonly referred to in the literature.

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