Predicting Student Performance Using Educational Data Mining: A Review Article Swipe
Veena Kumari
,
Areej Fatemah Meghji
,
Rohma Qadir
,
Urooj Gianchand
,
Farhan Bashir Shaikh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.51153/kjcis.v7i1.212
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.51153/kjcis.v7i1.212
Educational Data Mining (EDM) strategies facilitate the efficient and in-depth analysis of student data. EDM provides useful insights into comprehending student learning patterns and identifying factors that influence academic success. This review aims to evaluate the efficacy of classification algorithms popularly explored in EDM for predicting student performance and identifying common trends in existing EDM research. The review follows a systematic approach, relevant research articles have been cited following an inclusion and exclusion criteria to ensure the selection of studies that specifically address the use of EDM techniques for predicting student academic achievement. According to the review findings, most researchers have utilized the features of cumulative grade point average, internal and external assessment, and demographic information to predict student performance. The most common techniques in EDM for predicting students’ performance are Naïve Bayes and Decision Trees. The review also focuses on the potential for bias, key examination of challenges, and possible future directions in the field. In the context of student performance prediction, ethical considerations regarding privacy, data handling, and the interpretation of results are also identified
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All OpenAlex metadata
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https://openalex.org/W4403657877Canonical identifier for this work in OpenAlex
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https://doi.org/10.51153/kjcis.v7i1.212Digital Object Identifier
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Predicting Student Performance Using Educational Data Mining: A ReviewWork title
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reviewOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-10-21Full publication date if available
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Veena Kumari, Areej Fatemah Meghji, Rohma Qadir, Urooj Gianchand, Farhan Bashir ShaikhList of authors in order
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https://doi.org/10.51153/kjcis.v7i1.212Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.51153/kjcis.v7i1.212Direct OA link when available
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Educational data mining, Data science, Computer science, Mathematics education, Data mining, PsychologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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