Machine Learning for Predicting Student Performance: A Systematic Review
Abstract
Due to the abundance of data in the educational system, predicting student success has grown more challenging. Nowadays, the majority
of educational institutions apply machine learning approaches to enhance their systems. These strategies are used to analyse student
performance and assist students in enhancing their performance. Therefore, a thorough analysis of all articles in this area is required
to comprehend the application of machine learning techniques in education and how to forecast student success. This study focuses
on identifying the critical variables that the authors believe have a significant impact on students’ academic achievement, as well as the
predominant prediction techniques.
References
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24. Adekitan,A.I.,&Salau,O.P.(2019).Theimpactofengineeringstudents'performance in the first three years on their graduation result using educational datamining.Heliyon.
25. Al-Sudani,Sahar&Palaniappan,Ramaswamy.(2019).Predictingstudents‘finaldegreeclassificationusinganextendedprofile.EducationandInformationTechnologies.1-13. 10.1007/s10639-019-09873-8.
26. Hasan, H. R., Rabby, A. S. A., Islam, M. T., & Hossain, S. A. (2019, July). MachineLearning Algorithm for Student's Performance Prediction. In 2019 10th InternationalConferenceonComputing,CommunicationandNetworkingTechnologies(ICCCNT)(pp. 1-7).IEEE.
27. G. Gray, C. McGuinness, P. Owende, Anapplication of classification models topredict learner progression in tertiary education, in Advance Computing Conference(IACC),2014IEEEInternational,IEEE,2014, pp.549–554.
28. Lau, E. & Sun, L. & Yang, Qingping. (2019). Modelling, prediction and classificationofstudentacademicperformanceusingartificialneuralnetworks.SNAppliedSciences.1. 10.1007/s42452-019-0884-7.
29. Turabieh, H. (2019, October). Hybrid Machine Learning Classifiers to Predict StudentPerformance. In 2019 2nd International Conference on new Trends in ComputingSciences(ICTCS) (pp. 1-6).IEEE.
30. Livieris, I. E., Drakopoulou, K., Tampakas, V. T., Mikropoulos, T. A., &Pintelas, P.(2019). Predicting secondary school students' performance utilizing a semi-supervisedlearningapproach. Journal of educationalcomputingresearch, 57(2), 448-470.
31. Kumar M. and Singh A.J. (2019). Performance Analysis of Students Using MachineLearning&DataMiningApproach.InternationalJournalofEngineeringandAdvancedTechnology(IJEAT). 75-79.
32. Buenaño-Fernández, Diego & Gil, David &Luján-Mora, Sergio. (2019). Applicationof Machine Learning in Predicting Performance for Computer Engineering Students:ACaseStudy. Sustainability. 11. 10.3390/su11102833.
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2. N Mccrea, ‗An Introduction to Machine Learning Theory and Its Applications: AVisual Tutorial with Examples‘, https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer
3. U. bin Mat, N. Buniyamin, P. M. Arsad, R. Kassim, An overview of using academicanalyticstopredictandimprovestudents‘achievement:Aproposedproactiveintelligentintervention,in:EngineeringEducation(ICEED),2013IEEE5thConferenceon,IEEE,2013, pp. 126–130.
4. http://dataaspirant.com/2014/09/19/supervised-and-unsupervised-learning/
5. https://www.datascience.com/blog/regression-and-classification-machine-learning- algorithms
6. http://www.differencebetween.net/technology/differences-between-supervised-learning-and-unsupervised-learning/
7. Fok W., He Y., Law K., CheungKH and Ho YY., ―Prediction model for students' future development by deep learning and tensorflow artificial intelligence engine," 4thInternational Conference on Information Management (ICIM), Oxford, UK, 25-27May2018, pp. 103-106.
8. AlyW.,HegazyO.andRashadHeba,―AutomatedstudentAdvisoryusingMachine Learning‖ InternationalJournalofComputerApplications, Vol81(19),pp.19-24,November 2013. [Online]. Available:https://www.researchgate.net/publication/258885427
9. B. Guo, R. Zhang, G. Xu, C. Shi and L. Yang, "Predicting Students Performance inEducationalDataMining," 2015InternationalSymposiumonEducationalTechnology (ISET), Wuhan, 2015, pp. 125-128.
10. Singh Mamtaand Singh Jyoti, ―Machine Learning Techniques for prediction of subject scores: A comparative study‖, International Journal of Computer Science andNetwork,Vol2,issue4,pp.77-80,August2013.[Online].Available:https://pdfs.semanticscholar.org/2368/3634d0999020d6a90bf79fa605ceebe90891.pdf
11. BalachewErmiyasB.andGobenaFeiduA.,―Studentperformancepredictionmodel using Machine Learning Approach: The case of wolkiteUniversity,‖InternationalJournal of Advanced Research in Computer Science and Software Engineering, Vol 7,Issue2,pp.46-50,February2017.[Online].Available:https://pdfs.semanticscholar.org/9c6d/937d4d015f061e519f627132c6db60fb56ad.pdf
12. GorikhanN.andAbdullahS.(2016),―AstudyonImplementationofClassification techniquestopredictstudentsforInstitutionalAnalysis‖https://bspace.buid.ac.ae/bitstream/1234/805/1/2013310009.pdf
13. Bergin S.and ReillyR., ―Statistical and Machine Learning models to Predict Programming Performance,‖ Ph.D. dissertation, Dept. Computer Science, NationalUniversityofIreland, Maynooth,Ireland, 2006.
14. T.Mishra,D.KumarandS.Gupta,"MiningStudents'DataforPredictionPerformance," 2014FourthInternationalConferenceonAdvancedComputing&CommunicationTechnologies, Rohtak, 2014, pp. 255-262.
15. M. Han, M. Tong, M. Chen, J. Liu and C. Liu, "Application of Ensemble AlgorithminStudents'PerformancePrediction," 20176thIIAIInternationalConferenceonAdvancedAppliedInformatics(IIAI-AAI),Hamamatsu,2017, pp.735-740.
16.SungarV.,ShindePoojaandRupnarMonali(2017).PredictingStudent‘sPerformanceusingMachineLearning.CommunicationsonAppliedElectronics(CAE), Foundation of Computer Science FCS, New York, USA, Volume 7 – No. 11,December 2017. [Online]. Available:https://www.caeaccess.org/archives/volume7/number11/sungar-2017-cae-652730.pdf
17. T. Mahboob, S. Irfan and A. Karamat, "A machine learning approach for studentassessment in E-learning using Quinlan's C4.5,Naive Bayes and Random Forestalgorithms," 2016 19th International Multi-Topic Conference (INMIC),Islamabad,2016, pp.1-8.
18. E. Tanuar, Y. Heryadi, Lukas, B. S. Abbas and F. L. Gaol, "Using Machine LearningTechniques to Earlier Predict Student's Performance," 2018 IndonesianAssociationfor Pattern Recognition International Conference (INAPR), Jakarta, Indonesia, 2018,pp. 85-89.
19. M. Solis, T. Moreira, R. Gonzalez, T. Fernandez and M. Hernandez, "Perspectives toPredictDropoutinUniversityStudentswithMachineLearning," 2018IEEEInternationalWorkConferenceonBioinspiredIntelligence(IWOBI),SanCarlos,2018, pp.1-6.
20. S. K. Pushpa, T. N. Manjunath, T. V. Mrunal, A. Singh and C. Suhas, "Class resultpredictionusingmachinelearning," 2017InternationalConferenceonSmartTechnologiesForSmartNation(SmartTechCon),Bangalore,2017, pp.1208-1212.
21. GerritsenL.andConijnR.,―PredictingstudentperformancewithNeuralNetworks,‖dissertation,Dept. Humanities,TilburgUniversity, TheNetherlands, May2017.
22. Roy, Sagardeep& Garg, Anchal. (2017). Predicting academic performance of Studentusingclassification techniques. 568-572. 10.1109/UPCON.2017.8251112.
23. Rahman, Md & Islam, Md. (2017). Predict Student's Academic Performance andEvaluate the Impact of Different Attributes on the Performance Using Data MiningTechniques.1-4. 10.1109/CEEE.2017.8412892.
24. Adekitan,A.I.,&Salau,O.P.(2019).Theimpactofengineeringstudents'performance in the first three years on their graduation result using educational datamining.Heliyon.
25. Al-Sudani,Sahar&Palaniappan,Ramaswamy.(2019).Predictingstudents‘finaldegreeclassificationusinganextendedprofile.EducationandInformationTechnologies.1-13. 10.1007/s10639-019-09873-8.
26. Hasan, H. R., Rabby, A. S. A., Islam, M. T., & Hossain, S. A. (2019, July). MachineLearning Algorithm for Student's Performance Prediction. In 2019 10th InternationalConferenceonComputing,CommunicationandNetworkingTechnologies(ICCCNT)(pp. 1-7).IEEE.
27. G. Gray, C. McGuinness, P. Owende, Anapplication of classification models topredict learner progression in tertiary education, in Advance Computing Conference(IACC),2014IEEEInternational,IEEE,2014, pp.549–554.
28. Lau, E. & Sun, L. & Yang, Qingping. (2019). Modelling, prediction and classificationofstudentacademicperformanceusingartificialneuralnetworks.SNAppliedSciences.1. 10.1007/s42452-019-0884-7.
29. Turabieh, H. (2019, October). Hybrid Machine Learning Classifiers to Predict StudentPerformance. In 2019 2nd International Conference on new Trends in ComputingSciences(ICTCS) (pp. 1-6).IEEE.
30. Livieris, I. E., Drakopoulou, K., Tampakas, V. T., Mikropoulos, T. A., &Pintelas, P.(2019). Predicting secondary school students' performance utilizing a semi-supervisedlearningapproach. Journal of educationalcomputingresearch, 57(2), 448-470.
31. Kumar M. and Singh A.J. (2019). Performance Analysis of Students Using MachineLearning&DataMiningApproach.InternationalJournalofEngineeringandAdvancedTechnology(IJEAT). 75-79.
32. Buenaño-Fernández, Diego & Gil, David &Luján-Mora, Sergio. (2019). Applicationof Machine Learning in Predicting Performance for Computer Engineering Students:ACaseStudy. Sustainability. 11. 10.3390/su11102833.
33. MahmoudAbuZohair,Lubna.(2019).PredictionofStudent‘sperformancebymodellingsmall dataset size. 16. 18. 10.1186/s41239-019-0160-3.
Published
2023-10-13
How to Cite
., Mehak.
Machine Learning for Predicting Student Performance: A Systematic Review.
Journal of Advanced Research in Networking and Communication Engineering, [S.l.], v. 6, n. 1, p. 15-21, oct. 2023.
Available at: <http://thejournalshouse.com/index.php/adr-networking-communication-eng/article/view/863>. Date accessed: 24 apr. 2025.
Section
Review Article
