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Utilizing random forest algorithm for early detection of academic underperformance in open learning environments.

Shikah Abdullah Albriki Balabied1, Hala F Eid2

  • 1Department of Quality of Life and Continuing Education, College of Education and Human Development, University of Bisha, Bisha, Saudi Arabia.

Peerj. Computer Science
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

Early prediction models identify at-risk students in Open Learning Environments (OLEs). This approach aids timely interventions, improving academic success for a scalable educational model.

Keywords:
Learning analyticsMOOCsOULADOpen learning environmentsRandom forest algorithm

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Area of Science:

  • Educational Technology
  • Learning Analytics
  • Data Science

Background:

  • Open Learning Environments (OLEs) offer scalable, accessible education globally across diverse subjects.
  • The scalability of OLEs presents challenges in providing personalized student support and feedback.
  • Early prediction of student performance is crucial for timely interventions and improved learning experiences.

Purpose of the Study:

  • To develop a predictive model for identifying at-risk students within OLEs.
  • To enable timely interventions that promote student academic achievement.

Main Methods:

  • Utilized the random forest classifier model.
  • Analyzed anonymized large datasets from Open University Learning Analytics (OULAD).
  • Identified patterns and factors contributing to student success or failure.

Main Results:

  • The developed algorithm achieved 90% accuracy in identifying at-risk students.
  • The model effectively predicts students who may require additional support.

Conclusions:

  • Early identification of at-risk students is feasible and accurate using machine learning models.
  • Predictive analytics in OLEs can facilitate targeted support, enhancing student outcomes.