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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Predicting students' performance in English and Mathematics using data mining techniques.

Muhammad Haziq Bin Roslan1, Chwen Jen Chen1

  • 1Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Malaysia.

Education and Information Technologies
|August 3, 2022
PubMed
Summary
This summary is machine-generated.

Data mining techniques effectively predict secondary school students' performance in English and Mathematics. Past academic achievement is the strongest predictor, highlighting the interconnectedness of these subjects for targeted interventions.

Keywords:
Data mining techniquesEducational data miningEnglishMathematicsPerformance predictionSecondary education

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Area of Science:

  • Educational Data Mining
  • Student Performance Prediction
  • Learning Analytics

Background:

  • Student performance in English and Mathematics is a global concern.
  • Identifying predictors and effective prediction techniques is crucial for academic improvement.

Purpose of the Study:

  • To predict secondary school students' performance in English and Mathematics using data mining.
  • To identify key predictors (academic, demographic, psychological) of student performance.
  • To determine the most effective data mining techniques for performance prediction and understand the relationship between English and Mathematics performance.

Main Methods:

  • Utilized archival data of 16-year-old students (2019 cohort) for Malaysian Certificate of Examination (2021).
  • Employed data mining techniques via Orange software, including Decision Tree (DT) and Naïve Bayes (NB).
  • Applied DT rules to characterize students across performance levels (low, moderate, high).

Main Results:

  • Decision Tree and Naïve Bayes techniques demonstrated the best predictive performance for English and Mathematics, respectively.
  • Past academic performance emerged as the most critical predictor, followed by specific demographic and psychological attributes.
  • A significant interrelationship was found: past Mathematics performance predicted English performance, and past English performance predicted Mathematics performance.

Conclusions:

  • Data mining offers valuable insights into factors influencing student performance in core subjects.
  • Understanding student characteristics and predictive relationships can inform targeted interventions to enhance academic outcomes.
  • The strong correlation between English and Mathematics performance underscores the need for integrated learning approaches.