Implementation of an Intelligent Course Advisory Expert System

Abstract

Abstract-Academic advising of students is an expert task that requires a lot of time, and intellectual investments from the human agent saddled with such a responsibility. In addition, good quality academic advising is subject to availability of experienced and committed personnel to undertake the task. However, there are instances when there is paucity of capable human adviser, or where qualified persons are not readily available because of other pressing commitments, which will make system-based decision support desirable and useful. In this work, we present the design and implementation of an intelligent Course Advisory Expert System (CAES) that uses a combination of rule based reasoning (RBR) and case based reasoning (CBR) to recommend courses that a student should register in a specific semester, by making recommendation based on the student's academic history. The evaluation of CAES yielded satisfactory performance in terms of credibility of its recommendations and usability.

Academic Advising Systems: A Systematic Literature Review of Empirical Evidence

“…Many HEI offer AAS designed to help students and their academic advisors recommend learning resources and review degree requirements and the student's progress towards the intended degree. However, existing advising support software tools can augment the student-advisor relationship, but cannot and should not replace in-person advising [6,8,16,18,58]. This is the point at which learning science, psychology, pedagogy, and computer science intersect.…”

“…One crucial issue in this category that authors attempt to address is how to propose careful recommendations for different courses according to students' goals and preferences. More specifically, the authors seek to prepare course lists that satisfy several student and university constraints, some of which depend on individual student cases [2,5,6,22,24,27,28,50]. In some case studies, authors choose to apply computational, intelligence-based algorithms to reach a degree of automatic advising by combining genetic algorithms with decision trees for developing the short-term curricular schedule, as well as by combining perception marks with the registered courses [41] or by assisting in data mining and intelligent adaptive fuzzy logic for implementing an elective course suggestion system [44].…”

“…In paper [37], the authors present recommendations of student courses using ant colony optimization and concluded that their solution is promising, since it overcomes most of the disadvantages of classical approaches based on collaborative filtering in terms of performance. AAS proposed in [24] employ recommendations based on decision support tools, while other authors [33,34], use expert systems combined with semantic infostructures [22,28,47], or database management systems [6]. The course recommendation problem gets more complex when considering a large number of courses that have different weights, values, and priorities addressed by the Course-Petri net, a specialization of Petri net that is used on paper [26] as the foundation for development of an advising system.…”

“…This is unlike when an ontology is used for knowledge representation, which, although quite effective, require an advance investment in quality ontology development before efficient course advising can be obtained [6] Collaborative filtering-based…”

“…Cases in which a student has failed multiply in different departments and are more intricate to handle even for the human course adviser [6] • Authors in [44] observed that the success of students in their previous required courses and the student's skills are found to be determinants of student's success in elective courses…”