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Updated: Jul 9, 2025

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
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Student course grade prediction using the random forest algorithm: Analysis of predictors' importance.

Mirna Nachouki1, Elfadil A Mohamed1, Riyadh Mehdi1

  • 1Artificial Intelligence Research Centre, Department of Information Technology, Ajman University, UAE.

Trends in Neuroscience and Education
|December 4, 2023
PubMed
Summary
This summary is machine-generated.

Predicting student academic performance using machine learning helps universities identify at-risk students. Grade point average and high school scores are key predictors for improving student retention and success.

Keywords:
Course grade predictionEducational data miningInfluencing factorsRandom forest algorithmStudent performance

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Area of Science:

  • Educational Data Mining
  • Machine Learning in Education

Background:

  • Universities face challenges with student retention and dropout rates.
  • Predicting academic performance is crucial for early intervention and support.

Purpose of the Study:

  • To develop a predictive model for student course performance.
  • To identify key predictors influencing student academic outcomes.

Main Methods:

  • A random forest model was employed for prediction.
  • Seven input predictors were derived from student transcripts and recorded data.
  • The study analyzed data from 650 undergraduate computing students.

Main Results:

  • Grade point average and high school scores were the most significant predictors of course grades.
  • Course category and class attendance percentage showed equal importance.
  • Course delivery mode was found to have no significant effect on performance.

Conclusions:

  • The predictive model can identify courses that pose challenges for at-risk students.
  • Institutions can implement targeted actions and policies based on these predictions.
  • This approach supports strategies to enhance student completion rates and reduce dropouts.