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Research Article Open access CC BY 4.0

Knowledge Based Performance Evaluation and Predictive Model for Undergraduate Students

Ayodeji O. J. Ibitoye, Bunmi Borokini, Jesujoba O. Alabi

Asian Journal of Research in Computer Science · pp. 1–7 · Published 9 Feb 2019

10.9734/ajrcos/2018/v2i330074

Abstract

In educational data mining, the process of analysing and predicting from a pool of acquired data is a big challenge due to the influence of behavioural, environmental, parental, personal and social traits of students. While existing education predictive systems have used patterns generated from mined common factors to predict student performance based on subject, faculty, and grade amongst others, explicit traits, which defines a student are often neglected. Thus, such existing models are too general for specific and targeted analysis in more recent times when predictive features are although common but in real essence unique to individual students to a certain degree. Here, a Self-Academic Appraisal and Performance Predictive (SAAPP) system was developed to analyse and predict the overall performance of students before the expiration of their course duration. The inherent knowledge driven model analyses common available predictive internal and external factors, with probabilistic analysis of student academic history and pending courses. The system then builds a personal data centric system for individual student through a decision support expert system and a probabilistic optimal grade point analysis for more effective recommendation. The developed system is more accurate, reliable and precise in student performance classification with targeted recommendations.

Predictive analytics student performance evaluation educational data mining recommender system

References (9)

  1. 1 A Review on Predicting Student's Performance Using Data Mining Techniques [DOI]
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  3. 3 A comparative analysis of techniques for predicting academic performance [DOI]
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  6. 6 Institutional Factors Affecting the Academic Performance of Public Administration Students in a Nigerian University [DOI]
  7. 7 Predictive Modeling and Analysis of Student Academic Performance in an Engineering Dynamics Course [DOI]
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Cited by 5

Analysing and Predicting Student’s Performance Using their Surrounding Data

Bhumika Banswal, Anshula, Aaraika · International Conference on Computing Communication and Networking Technologies · 2023

A Regression Model and a Combination of Academic and Non-Academic Features to Predict Student Academic Performance

M. Arifin, W. Widowati, F. Farikhin · 2023

Comparative Analysis on Educational Data Mining Algorithm to Predict Academic Performance

Muhammad Arifin, Widowati, Farikhin · 2021 International Seminar on Application for Technology of Information and Communication (iSemantic) · 2021

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