A Systematic Study of the Literature on Career Guidance Expert Systems for Students: Implications for ODL

Authors

DOI:

https://doi.org/10.56059/jl4d.v9i3.648

Keywords:

Expert systems; rule-based, case-based, fuzzy logic, data mining, artificial neural network

Abstract

The continual evolution of employment opportunities in the present industrial era has raised the need for career-long expert advice. Similar to other fields, thankfully technology has come to our rescue in the area of career guidance also. This paper presents a systematic review of Expert Systems (ES) developed for career guidance, course selection and evaluation of students in the past ten years. The popular research databases Google Scholar and Science Direct were used for obtaining the relevant research papers through broad keywords. The keywords were refined to identify the articles related to rule-based, case-based and fuzzy logic-based ES used for career guidance. A total of twenty-five peer-reviewed relevant articles with full-text available online was selected for the final study. In order to avoid duplicity, technical reports and unreferenced literature were excluded. The review identifies the relatively high weight given by the researchers to rule-based systems owing to their simplicity and broad applicability. However, the relative merits and demerits of rule-based, case-based and fuzzy logic-based ES are highly dependent on the field of application. Nevertheless, ES find wide applications in the area of career guidance and have the potential to enhance the career guidance accessibility of the most remote students.

Author Biography

Shilpa Gunwant, Uttarakhand Open University

Shilpa Gunwant is based in the Department of Computer Science, Uttarakhand Open University, Haldwani, Uttarakhand, India. Email: shilpa15aneja@gmail.com 

Published

2022-11-21

How to Cite

Gunwant, S. (2022). A Systematic Study of the Literature on Career Guidance Expert Systems for Students: Implications for ODL. Journal of Learning for Development, 9(3), 492–508. https://doi.org/10.56059/jl4d.v9i3.648

Issue

Section

Research Articles