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A meta-analysis of educational data mining for predicting students performance in programming

dc.contributor.authorMoonsamy, Devrajen_US
dc.contributor.authorNaicker, Nalindrenen_US
dc.contributor.authorAdeliyi, Timothy T.en_US
dc.contributor.authorOgunsakin, Ropo E.en_US
dc.date.accessioned2022-05-16T13:45:35Z
dc.date.available2022-05-16T13:45:35Z
dc.date.issued2021-02
dc.date.updated2022-05-11T13:18:56Z
dc.description.abstractAn essential skill amid the 4th industrial revolution is the ability to write good computer programs. Therefore, higher education institutions are offering computer programming as a module not only in computer related programmes but other programmes as well. However, the number of students that underperform in programming is significantly higher than the non-programming modules. It is, therefore, crucial to be able to accurately predict the performance of students pursuing programming since this will help in identifying students that may underperform and the necessary support interventions can be timeously put in place to assist these students. The objective of this study is therefore to obtain the most effective Educational Data Mining approaches used to identify those students that may underperform in computer programming. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analysis) approach was used in conducting the meta-analysis. The databases searched were, namely, ACM, Google Scholar, IEEE, Pro-Quest, Science Direct and Scopus. A total of 11 scientific research publications were included in the meta-analysis for this study from 220 articles identified through database searching. The residual amount of heterogeneity was high (τ2 = 0.03; heterogeneity I2 = 99.46% with heterogeneity chi-square = 1210.91, a degree of freedom = 10 and P = >0.001). The estimated pooled performance of the algorithms was 24% (95% CI (13%, 35%). Meta-regression analysis indicated that none of the moderators included have influenced the heterogeneity of studies. The result of effect estimates against its standard error indicated publication bias with a P-value of 0.013. These meta-analysis findings indicated that the pooled estimate of algorithms is high.en_US
dc.format.extent8 p.en_US
dc.identifier.citationMoonsamy, D., Naicker, N., Adeliyi, T.T. and Ogunsakin, R.E. 2021. A meta-analysis of educational data mining for predicting students performance in Programming. International Journal of Advanced Computer Science and Applications. 12(2): 97-104. doi:10.14569/ijacsa.2021.0120213en_US
dc.identifier.doi10.14569/ijacsa.2021.0120213
dc.identifier.issn2158-107X
dc.identifier.issn2156-5570 (Online)
dc.identifier.otherisidoc: QY7AR
dc.identifier.urihttps://hdl.handle.net/10321/3967
dc.language.isoenen_US
dc.publisherThe Science and Information Organizationen_US
dc.relation.ispartofInternational Journal of Advanced Computer Science and Applications; Vol. 12, Issue 2en_US
dc.subjectData miningen_US
dc.subjectEducational data miningen_US
dc.subjectMachine learningen_US
dc.subjectPerformanceen_US
dc.subjectProgrammingen_US
dc.subject0803 Computer Softwareen_US
dc.subject1005 Communications Technologiesen_US
dc.titleA meta-analysis of educational data mining for predicting students performance in programmingen_US
dc.typeArticleen_US
local.sdgSDG04

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