Applying AI Models to Analyze Student Learning Interests Through Digital Interaction Patterns
DOI:
https://doi.org/10.63876/ijtm.v2i3.142Keywords:
Artificial Intelligence (AI), Student Learning Interests, Digital Interaction Patterns, Educational Data Mining, Personalized Learning, Machine Learning in EducationAbstract
In the digital era, students increasingly engage with learning platforms that generate vast amounts of interaction data. This study explores the application of Artificial Intelligence (AI) models to analyze students' learning interests based on their digital interaction patterns. By leveraging machine learning algorithms and behavioral analytics, we identify correlations between user activities—such as clickstreams, time spent on content, and interaction frequencies—and subject preferences. The study utilizes a dataset from an online learning management system and applies classification and clustering techniques to detect interest trends among students. Results show that AI models can effectively predict individual learning preferences and offer insights to personalize educational content. These findings highlight the potential of integrating AI-driven analytics in education to enhance learner engagement and optimize teaching strategies.
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