The Learning Style Decoder: FSLSM-Guided Behavior Mapping Meets Deep Neural Prediction in LMS Settings

Personalized learning environments increasingly rely on learner modeling techniques that integrate both explicit and implicit data sources. This study introduces a hybrid profiling methodology that combines psychometric data from an extended Felder–Silverman Learning Style Model questionnaire with behavioral analytics derived from Moodle Learning Management System interaction logs. A structured mapping process is employed to associate over 200 unique log event types with FSLSM cognitive dimensions, enabling dynamic, behavior-driven learner profiles. To evaluate the effectiveness and generalizability of the approach, experiments were conducted across three large-scale datasets: a university dataset from the International Hellenic University, a public dataset from Kaggle, and a combined dataset totaling over 7 million log entries. Deep learning models—including a Sequential Neural Network, BiLSTM, and a pretrained MLSTM-FCN—were trained to predict student performance across regression and classification tasks. Results show high accuracy in binary classification, moderate success in 3-class prediction, and limited effectiveness in fine-grained grade regression and 11-class classification. The proposed framework demonstrates the potential of combining static and behavioral data for scalable personalization in smart learning systems, paving the way for more adaptive, data-driven educational interventions.