Advancing Learning Analytics: Detect and Predict Confusion in Learners Through AI

As digital education platforms continue to evolve, understanding learner engagement and emotional states has become critical for improving academic outcomes and reducing dropout rates. This thesis explores the detection and prediction of confusion—an essential epistemic emotion that influences positive and negative learning experiences. Using clickstream data from online platforms, this study develops predictive models with AI, specifically focusing on confusion in online learning. The methodology integrates clustering, time series analysis, and advanced AI algorithms, including Generative AI, to detect confusion patterns and provide real-time interventions. Results indicate that predictive modelling based on clickstream data can effectively identify confusion and its influence on learner engagement. This research offers a framework for improving learning analytics, contributing to personalised learning experiences and broader educational interventions.

Author dc:creator

• Samani, Chaitali J.

Statement dc:rights

• info:eu-repo/semantics/openAccess
• The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
• © 2025 Chaitali Samani
• au.edu.uts.lib/cph

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10453/193134

OAI identifier oai:identifier

oai:opus.lib.uts.edu.au:10453/193134