A rule induction framework on the effect of 'negative' attributes to academic performance

College

Gokongwei College of Engineering

Department/Unit

Mechanical Engineering

Document Type

Article

Source Title

International Journal of Emerging Technologies

Volume

16

Issue

15

First Page

31

Last Page

45

Publication Date

2021

Abstract

Attaining high retention rates among engineering institutions is a predominant issue. A significant portion of engineering students face challenges of retention. Academic advising was implemented to resolve the issue. Decision support systems were developed to support the endeavor. Machine learning have been integrated among such systems in predicting student performance accurately. Most works, however, rely on a black box model approach. Rule induction generates simpler if-then rules, exhibiting clearer understanding. As most research works considered attributes for positive academic performance, there is the need to consider ‘negative’ attributes. ‘Negative’ attributes are critical indicators to possibility of failure. This work applied rule induction techniques for course grade prediction using ‘negative’ attributes. The dataset is the academic performance of 48 mechanical engineering students taking a machine design course. Students’ attributes on workload, course repetition, and incurred absences are the predictors. This work implemented two rule induction techniques, rough set theory (RST) and adaptive neuro fuzzy inference system (FIS). Both models attained a classification accuracy of 70.83% with better performance for course grades of ‘Pass’ and ‘High’. RST generated 16 crisp rules while ANFIS generated 27 fuzzy rules, yielding significant insights. Results of this study can be used for comparative analysis of student traits between institutions. The illustrated framework can be used in formulating linguistic rules of other institutions.

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Disciplines

Educational Psychology | Mechanical Engineering

Keywords

Academic achievement; Engineering students—Psychology; Machine learning

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