Hybrid Learner Profiling Using Behavioral Traces and LLM-Based Semantic Analysis
This study proposes a hybrid learner profiling framework that combines behavioral data and semantic analysis based on Large Language Models (LLMs) to build interpretable and actionable learner representations.
Existing learning analytics approaches rely primarily on quantitative interaction logs or static self-reports, failing to capture the underlying cognitive and emotional reasons behind learner behaviors. Most existing profiling approaches describe what the learner does, but not why they behave in a certain way.
The framework relies on two complementary pillars. The first analyzes data from virtual learning environments (VLEs). The second uses an LLM as a semantic annotator to extract cognitive, self-regulatory, and emotional dimensions from learners’ textual outputs. The approach was validated using real-world offline datasets and synthetic pedagogical archetypes.
This work contributes a multidimensional learner representation that bridges the gap between observable interaction logs and unobservable internal states.
The behavioral profiling module achieved a dropout prediction AUC of 0.871. Semantic annotation showed no correlation between cognitive complexity and emotional tone (r = 0.00). In controlled validation, planning cues were detected in 92.3% of valid Strategist messages, compared with 0-16.7% of messages from the other archetypes.
Researchers should treat learner profiling as a multidimensional and partially latent construct requiring complementary data sources.
Researchers should consider learner profiling as a multidimensional and partially latent construct, combining multiple data sources rather than relying solely on easily accessible interaction logs.
This framework helps educational organizations reduce dropout rates and improve the overall quality and inclusivity of digital learning environments.
Future developments will focus on deploying the framework in real-world settings, such as integrating it into a real Learning Management System (LMS) and evaluating its impact on adaptive learning and personalized support.
Back