September 12, 2025Indian Journal of Science and TechnologyOpen Access

Predicting Student Outcomes through Cognitive Behaviour Analysis in Online Collaborative Learning Using Big Data and Machine Learning

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The factorization machine classifier achieved a prediction accuracy of 91.8%, outpacing competitor models.
Peer feedback and interaction frequency were identified as key factors, enhancing model prediction by 22% over the baseline.
Using a dataset of 5,000 student records, models were evaluated based on accuracy, precision, and recall metrics.
This study highlights the utility of machine learning models for personalized educational analytics in collaborative environments.

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Implication

Analysis shows that machine learning models improve student engagement outcomes in online collaborative settings, indicating the potential for adaptive learning tools.

Cite This Study

Vijayalakshmi et al. (2025) studied this question.

synapsesocial.com/papers/68d41c02713b0b5dfea65452 https://doi.org/10.17485/ijst/v18i29.563

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Implication

Analysis shows that machine learning models improve student engagement outcomes in online collaborative settings, indicating the potential for adaptive learning tools.

Predicting Student Outcomes through Cognitive Behaviour Analysis in Online Collaborative Learning Using Big Data and Machine Learning

Key Points

Discussion

Implication

Cite This Study

Predicting Student Outcomes through Cognitive Behaviour Analysis in Online Collaborative Learning Using Big Data and Machine Learning

Key Points

Discussion

Implication

Cite This Study

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