Analyzing click data with AI

Implications for student performance prediction and learning assessment

Bibliographic Data

ID22167075
AuthorsMahdi-Reza Borna (Tarbiat Modares University), Hanan Saadat (Tarbiat Modares University), Aref Tavassoli Hojjati (Imam Khomeini International University), Elham Akbari (0000-0001-9285-6823, Tarbiat Modares University, corresponding author)
Year2024
Volume9
Publication date2024-12-20
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Education (JOURNAL)
Journal identifiersISSN: 2504-284X • E-ISSN: 2504-284X
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/feduc.2024.1421479
OpenAlexW4405645612
LanguageEN
Citations received4
References cited32

Background As the intersection of artificial intelligence (AI) and education deepens, predictive analytics using machine learning (ML) and deep learning (DL) models offer novel approaches to assessing student performance in online environments. However, challenges remain in accurately predicting high achievers and identifying students at risk due to limitations in traditional assessment models. This study explores the capabilities of these models in predicting academic achievement and highlights their potential role in reshaping educational assessment paradigms. Objectives To evaluate the efficacy of various AI models—including Random Forest, XGBoost, and recurrent neural networks (RNNs)—in identifying at-risk students and differentiating levels of academic achievement, with an emphasis on inclusive and adaptive educational assessments. A key focus is on leveraging these models to create more inclusive and adaptive educational assessments. Methods We analyzed a dataset comprising interaction data from the Open University Learning Analytics Dataset (OULAD), which includes clickstream data on student interactions with course materials from over 32,000 students. The models were trained and evaluated using performance metrics such as accuracy, precision, recall, and F1-scores, specifically targeting predictions of student withdrawals and distinctions. Results The models effectively identified students at risk of withdrawing, with the Random Forest model achieving an accuracy of 78.68% and deep learning models approximately 77%. However, accurately predicting high achievers posed challenges, suggesting a complex relationship between interaction data and academic success. This limitation underscores the need for more nuanced modeling approaches to improve predictions for top-performing students. Conclusion This research demonstrates the promise of AI-driven models in enhancing educational assessments while also highlighting current limitations in capturing academic excellence. Our findings indicate a need for ongoing development of AI tools that are ethically designed and capable of supporting dynamic, inclusive assessment strategies. Future research should focus on incorporating additional factors, such as student motivation and study behaviors, to enhance predictive accuracy, particularly for high achievers. Such advancements can contribute to a more equitable and effective educational landscape

Data science · Machine learning · Artificial Intelligence in Healthcare and Education · Computer Science · E-Learning and Knowledge Management · Online Learning and Analytics · Artificial Intelligence

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