Predicting students’ performance in English and Mathematics using data mining techniques
This study attempts to predict secondary school students’ performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students’ performance in English and Mathematics, characteristics of students with diferent levels of performa...
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| Main Authors: | , |
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| Format: | Article |
| Language: | en |
| Published: |
SPRINGER
2022
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/40463/1/Predicting%20students%E2%80%99%20performance%20in%20English-1.pdf http://ir.unimas.my/id/eprint/40463/ https://link.springer.com/article/10.1007/s10639-022-11259-2 https://doi.org/10.1007/s10639-022-11259-2 |
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| Summary: | This study attempts to predict secondary school students’ performance in English
and Mathematics subjects using data mining (DM) techniques. It aims to provide
insights into predictors of students’ performance in English and Mathematics, characteristics of students with diferent levels of performance, the most efective DM
technique for students’ performance prediction, and the relationship between these
two subjects. The study employed the archival data of students who were 16 years
old in 2019 and sat for the Malaysian Certifcate of Examination (MCE) in 2021.
The learning of English and Mathematics is a concern in many countries. Three
main factors, namely students’ past academic performance, demographics, and psychological attributes were scrutinized to identify their impact on the prediction. This
study utilized the Orange software for the DM process. It employed Decision Tree
(DT) rules to determine the characteristics of students with low, moderate, and high
performance in English and Mathematics subjects. DT and Naïve Bayes (NB) techniques show the best predictive performance for English and Mathematics subjects,
respectively. Such characteristics and predictions may cue appropriate interventions
to improve students’ performance in these subjects. This study revealed students’
past academic performance as the most critical predictor, as well as a few demographics and psychological attributes. By examining top predictors derived using
four diferent classifer types, this study found that students’ past Mathematics performance predicts their MCE English performance and students’ past English performance predicts their MCE Mathematics performance. This fnding shows students’ performances in both subjects are interrelated |
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