Performance prediction for undergraduate degree programs using machine learning techniques - a preliminary review

Waqar Un Nisa; Mudasser Naseer; Muhammad Atif; Salwa Muhammad Akhtar; Mehr Un Nisa

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Performance prediction for undergraduate degree programs using machine learning techniques - a preliminary review

Author(s)

Waqar Un Nisa Faculty of CS and IT, Virtual University of Pakistan,Lahore,Pakistan

Mudasser Naseer Department of and IT, The University of Lahore,Lahore,Pakistan

Muhammad Atif Department of and IT, The University of Lahore,Lahore,Pakistan

Salwa Muhammad Akhtar INS Department, HSM, University of Management and Technology,Lahore,Pakistan

Mehr Un Nisa Faculty of Science, Information Technology University,Lahore,Pakistan

Abstract

Academic Performance prediction for undergraduate students is considered as one of the hot research areas since last couple of decades. An accurate and timely prediction of the student's performance can directly influence the three participants; learner, instructor and the institution. This study presents a brief, preliminary review to explore existing literature from 2010 to 2022 in the context of performance prediction for Undergraduate Degree Programs (UDP). This review is organized according to Online and Traditional Education Systems (TES), and granularity level of performance output i.e., Degree program (Final CGPA), Next-semester, and the Course level grades. Aggregate analysis of the extracted data reveals that course level prediction is highly worked area deploying classification and regression techniques using data from academic domain. Existing empirical studies are mostly evaluated using accuracy, precision, recall and F1-measure and are validated with 10-fold cross validation. Contribution of this study is the novel categorical distribution of studies with respect to education system and granularity levels. Another important finding was the Success ratio of diferent Machine learning (ML) techniques used for these prediction studies. It is concluded that further research is required for TES to discover interdependent group of courses and Course Clusters for a certain degree program and then to develop prediction models for those course clusters.

Publication Details

Page(s) 45-60

DOI DOI not available

Published Journal: VAWKUM Transactions on Computer Sciences, Volume: 10, Issue: 2, Year: 2022

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Keywords

Supervised Learning, performance prediction, Grade prediction, Traditional Education

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