Institutional Data Analysis and Machine Learning Prediction of Student Performance

Rahanatu Suleiman, Rachid Anane

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Concern over student progression and retention is driving the systematic analysis of the vast amount of student data recorded in institutional data sets and in learning management systems (LMS) data sets. This is motivated by the belief that student analytics will help uncover patterns of behaviour and predict student performance, and thus facilitate the deployment of supportive educational interventions. This paper is concerned with the investigation of the institutional data of a Nigerian university, and the predictive impact of its attributes on student performance, measured in terms of cumulative grade point average (CGPA). The statistical analysis of the data set reveals that age and marital status have no significant relationship with final CGPA, whereas gender and pre-entry score have a weak relationship but are not good predictors. The application of four representative machine learning methods, namely linear regression, support vector regression, decision tree and random forests indicate that the third year CGPA is a good predictor of final year CGPA. A higher accuracy is achieved by using the aggregate value of the CGPAs of three previous years. Support vector regression has the best performance in predicting the final CGPA, whereas decision tree is the least performing model.

Original languageEnglish
Title of host publication2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
PublisherIEEE
Pages1480-1485
Number of pages6
ISBN (Electronic)978-1-6654-0527-0, :978-1-6654-0763-2
ISBN (Print)978-1-6654-0526-3
DOIs
Publication statusE-pub ahead of print - 20 May 2022
Event2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design - Hangzhou, China
Duration: 4 May 20226 May 2022

Publication series

Name2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

Conference

Conference2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design
Abbreviated title(CSCWD)
Country/TerritoryChina
CityHangzhou
Period4/05/226/05/22

Keywords

  • educational data
  • machine learning
  • regression
  • student analytics
  • student performance

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications

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