Jayasankar Santhosh

• Enhancing e-learning outcomes requires understanding students’ learning behaviors, particularly their affective and cognitive states. By recognizing and responding to these states, personalized interventions can be implemented to improve learning efficacy. This research aims to develop adaptive, learner-centric systems that integrate real-time mental state predictions with customized support, promoting both academic performance and student well-being in digital learning environments. To address the challenges in detecting learners’ mental states, this research focuses on developing innovative deep-learning solutions that integrate multimodal sensor data, enabling more accurate predictions and personalized interventions to enhance e-learning outcomes. We have developed and implemented innovative deep learning based models incorporating various validation methods and techniques to improve generalized prediction and personalized prediction models with significant improvements in overall accuracies. Notably, fine-tuning and user-specific calibration resulted in a substantial performance increase, with three-level stress detection using an LSTM model achieving a 56% improvement to reach 91% accuracy (F1=0.911) and a person-specific CNN-LSTM model showing a 50% improvement in detecting interest levels per participant. User-dependent approaches further enhanced accuracy, yielding gains of 13% for engagement, 19% for arousal, and 15% for valence. These refined models provide the foundation for personalized interventions designed to enhance the learning experience. These interventions include an adapted cognitive control training for managing distractions during online learning where the findings indicated that participants who received training exhibited significantly improved comprehension (p-value = 0.0059) and reduced distraction levels (p-value = 0.021) compared to the untrained group. Additionally, an application-based feedback system provides visual cues based on the learner’s current engagement and emotional state, enabling students to self-regulate and adapt their learning strategies in real time. Building on these interventions, a gaze-based adaptive learning system dynamically adjusts content presentation based on real-time engagement analysis. This system utilizes AI-generated summaries via ChatGPT to provide concise and relevant information when low engagement is detected, helping to re-engage students and maintain focus. User studies demonstrated significant improvements in comprehension (p = 0.0018), engagement (p = 0.0021), and overall learning outcomes, further highlighting the effectiveness of these personalized approaches.