Artificial Intelligence-driven Cognitive Diagnosis anAdaptive Learning: The Impact of Online Course Stickiness and Learning Skills

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dc.contributor.authorLiu, Chunmao
dc.contributor.authorTuntiwongwanich, Somkiat
dc.contributor.authorKantathanawat, Thiyaporn
dc.date.accessioned2026-08-06T10:49:47Z
dc.date.available2026-08-06T10:49:47Z
dc.date.issued2025-01-01
dc.description.abstractThe study analyzes behavioural data that express cognitive connotations, establishes AI-driven cognitive diagnosis and adaptive learning, fills the gap in multimodal data fusion in characterizing cognitive characteristics, constructs a holistic cognitive diagnosis model, and evaluates the impact of AI-driven adaptive learning models on online course stickiness and learning skills. The study innovatively proposes four components of the cognitive diagnosis model: the core theoretical framework of the model composed of cognitive dimensions, the input variables of the model consisting of behavioural data features, the diagnostic model as a method for calculating cognitive states, and the cognitive diagnosis output. The study sorted out the core theoretical framework of the model and proposed behavioural data feature input variables, and completed the cognitive diagnosis output in three dimensions: learning momentum, effectiveness, and strategy through the XGBoost model based on the Gradient Boosting framework.An adaptive online learning model based on behavioural data cognitive diagnosis and knowledge graph is proposed, which includes six parts: input layer, feature extraction layer, cognitive diagnosis module, learning path recommendation module and output layer. The cognitive diagnosis module uses the feature weights calculated by XGBoost as input to predict the mastery of knowledge points through LSTM improved by deep learning recurrent neural network (RNN) and makes learning recommendations based on knowledge graph, cognitive evaluation matrix CEM and collaborative filtering algorithm. Experimental results show that the adaptive learning model of behavioural data cognitive diagnosis has more advantages than traditional online learning, and the adaptive online learning model driven by artificial intelligence behavioural data mining can effectively improve course stickiness, learning skills and platform experience.
dc.identifier.citationJournal of Information Hiding and Multimedia Signal Processing, 16(2), 749-771, 2025
dc.identifier.issn20734212
dc.identifier.other2-s2.0-105009036479
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16610
dc.sourceJournal of Information Hiding and Multimedia Signal Processing
dc.subjectAdaptive learning
dc.subjectArtificial intelligence
dc.subjectBehavioural data mining
dc.subjectCognitive diagnosis
dc.subjectCourse stickiness
dc.subjectLearning skills
dc.titleArtificial Intelligence-driven Cognitive Diagnosis anAdaptive Learning: The Impact of Online Course Stickiness and Learning Skills
dc.typeArticle

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