The power of Deep Learning techniques for predicting student performance in Virtual Learning Environments: A systematic literature review

Alnasyan, Bayan; Basheri, Mohammed; Alassafi, Madini

doi:10.1016/j.caeai.2024.100231

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2024

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Latent Space Bias Mitigation for Predicting At-Risk Students

Al-Zawqari,

Peumans,

Vandersteen

2024

Computers and Education: Artificial Intelligence

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Latent Space Bias Mitigation for Predicting At-Risk Students

Al-Zawqari,

Peumans,

Vandersteen

2024

Computers and Education: Artificial Intelligence

View full text Add to dashboard Cite

Improving Online Education Through Constant Feedback Using Deep Learning and Integration with Large Language Models

Rojas,

Contero,

Montoya

2024

Preprint

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This study investigates the application of a deep learning-based predictive model to predict student performance. The objective was to enhance student performance by predicting and monitoring their academic activities, including attendance at synchronous sessions, interaction with digital content, participation in forums, and performance in portfolio creation tasks over an academic year. The predictive model was applied to an experimental group of students. Unlike the control group, which did not receive continuous feedback, the experimental group received personalized, continuous feedback based on predictions from a pre-trained model and interpreted by OpenAI’s GPT-4 language model. Significant improvements were observed in the performance of the experimental group compared to the control group. The average score on quizzes for the experimental group was 0.81, notably higher than the control group's 0.67. Recorded session engagement for the experimental group was 0.84, compared to 0.65 for the control group. Live session participation and forum activity were also significantly higher in the experimental group, with rates of 0.61 and 0.62 respectively, compared to the control group's 0.42 and 0.37. However, the average practice score was slightly higher in the control group, with a mean of 0.76 compared to 0.74 in the experimental group. Portfolio assessment scores were higher in the experimental group, with an average of 0.73 compared to 0.69 in the control group. These results support the hypothesis that using predictive models complemented by language models to provide continuous feedback improves learning effectiveness.

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The power of Deep Learning techniques for predicting student performance in Virtual Learning Environments: A systematic literature review

Cited by 2 publications

References 84 publications

Latent Space Bias Mitigation for Predicting At-Risk Students

Latent Space Bias Mitigation for Predicting At-Risk Students

Improving Online Education Through Constant Feedback Using Deep Learning and Integration with Large Language Models

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