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December 9, 2025ComputersOpen Access

Enhancing Student Engagement and Performance Through Personalized Study Plans in Online Learning: A Proof-of-Concept Pilot Study

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Authors

IKIndika KarunaratneUniversity of Moratuwa RSRami ShaniCalifornia Polytechnic State University VVVithanage Chethani Sandamali VithanageUniversity of Moratuwa

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Implication

Pilot study demonstrates predictive models improve academic outcomes in students using learning management systems, indicating effective personalized study plans.

Key Points

This research aims to enhance student engagement and performance through personalized study plans using data from learning management systems.
Analyzed the relationship between engagement and performance.
Developed predictive models for academic outcomes using machine learning techniques.
Generated customized study plan recommendations based on learner profiles.
Clustering analysis identified three learner profiles: high-engagement–high-performance, low-engagement–high-performance, and low-engagement–low-performance.
Support Vector Machines achieved 68.8% accuracy in classifying students across grade categories.
Personalized study plans significantly improved student performance in controlled experiments.

Cite This Study

Karunaratne et al. (2025) studied this question.

synapsesocial.com/papers/69401d682d562116f28f9129 https://doi.org/10.3390/computers14120538

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Implication

Pilot study demonstrates predictive models improve academic outcomes in students using learning management systems, indicating effective personalized study plans.

Enhancing Student Engagement and Performance Through Personalized Study Plans in Online Learning: A Proof-of-Concept Pilot Study

Discussion

Member takes

Implication

Key Points

Cite This Study

Enhancing Student Engagement and Performance Through Personalized Study Plans in Online Learning: A Proof-of-Concept Pilot Study

Discussion

Member takes

Implication

Key Points

Cite This Study

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