AI-Driven Adaptive E-Learning Using Real-Time Frustration Detection and Content Difficulty Adjustment
DOI:
https://doi.org/10.63665/IJAICE.0201.08Keywords:
Adaptive E-Learning, Artificial Intelligence, Learner Frustration Detection, Machine Learning, Behavioral Analytics, Personalized Learning, Sense-and-Adapt Framework, Content AdaptationAbstract
Adaptive e-learning demands a smart system based on adaptive mechanisms that can detect learners' problems and offer timely personalized support. This research suggests an Adaptive Content Selection and Frustration Classification based on a set of behavioral features and machine learning approaches using a Sense-and-Adapt framework. To generate a synthetic dataset with learner interaction records that are the largest number capable of being generated, a response behavior, error pattern, hint usage data type, engagement indicator, and completion attribute were employed. The following four classification models were investigated by analyzing the accuracy, precision, recall, F1 score and ROC-AUC: Logistic Regression, Random Forest, Support Vector Machine and Gradient Boosting. Logistic Regression achieved the highest classification performance with 98.80% accuracy, 98.73% F1-score and 99.93% ROC-AUC, over Random Forest, Support Vector Machine and Gradient Boosting models. This framework highlights the capabilities of AI-based sensing for learner states to adaptively scale difficulty and provide personalized learning experiences.
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