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Research Article Open access CC BY 4.0

Performance Analysis of Machine Learning Algorithms in Prediction of Student Academic Performance

Michael Donkor Adane, Joshua Kwabla Deku, Emmanuel Kwaku Asare

Journal of Advances in Mathematics and Computer Science · pp. 74–86 · Published 8 Mar 2023

10.9734/jamcs/2023/v38i51762

Abstract

The advancement in technology has contributed largely to the application of data mining in education in recent times. However, selecting appropriate algorithm(s) to “mine” knowledge about educational data presents a difficult challenge to researchers and analyst. This paper contributes to the use of classification algorithms in academic performance prediction. The predictive ability of four popular algorithms; C4.5 Decision tree (CDT), Multilayer Perceptron (MLP), Naïve Bayes (NB) and Random Forest (RF) algorithms were compared. The models were built using student dataset from selected private senior high schools in Ghana. The comparative analysis of the algorithms was made based on their Accuracy, Recall, Specificity, F-Measure and Running time. On all the training and test ratios; 80:20, 70:30 and 10-fold cross validation, the results indicated that all the algorithms performed well in the classification. However, the Naïve Bayes algorithm performed significantly better than the MLP and CDT on some ratios. The running time of the NB, CDT and RF were the quickest while MLP took the longest time.

Data Mining algorithms machine learning classification prediction student performance multilayer perceptron algorithm naïve bayes algorithm C4.5 decision tree

References (16)

  1. 1 An introduction to variable and feature selection [DOI]
  2. 2 Educational Data Mining: A Review of the State of the Art [DOI]
  3. 3 Using data mining to predict secondary school student performance
  4. 4 Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil [DOI]
  5. 5 Predicting Student Performance by Using Data Mining Methods for Classification [DOI]
  6. 6 Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques [DOI]
  7. 7 DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE
  8. 8 A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance [DOI]
  9. 9 Data Mining-based Design and Implementation of College Physical Education Performance Management and Analysis System [DOI]
  10. 10 Predictive Modeling and Analysis of Student Academic Performance in an Engineering Dynamics Course [DOI]
  11. 11 PREDICTING STUDENTS' PERFORMANCE THROUGH CLASSIFICATION: A CASE STUDY
  12. 12 A Comparative Analysis of Techniques for Predicting Academic Performance [DOI]
  13. 13 Performance of Classification Algorithms on Students' Data - A Comparative Study
  14. 14 Data mining: concepts and techniques [DOI]
  15. 15 Indian Journal Of Science And Technology [DOI]
  16. 16 Inference for the Generalization Error

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