Analyzing click data with AI: implications for student performance prediction and learning assessment - Borna, Mahdi-Reza; Saadat, Hanan; Hojjati, Aref Tavassoli; Akbari, Elham (Frontiers in Education, 2024)
BackgroundAs 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.ObjectivesTo 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.MethodsWe 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.ResultsThe 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.ConclusionThis 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.