With the increasing use of Virtual Learning Environments (VLEs), student performance prediction has become a necessary task to better academic outcomes, especially for disabled students who experience specific constraints. Machine learning (ML) models like Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN) have shown high predictive accuracy. However, these models often lack interpretability and making it difficult for educators to understand the key factors influencing student success. In addition, studies only concentrate on general student populations and do not incorporate complete demographic and behavioural data in their analyses, making it difficult to address the needs of disabled students. To address these challenges, this study proposed a novel approach by combining Long Short-Term Memory (LSTM) networks with SHapley Additive exPlanations (SHAP). LSTM captured the temporal dependencies in student interaction data, and SHAP make the model interpretable by identifying the most influential features responsible for achieving good learning performance. The proposed system allows for prediction accuracy and transparent decision making, thereby facilitating targeted interventions for disabled students. The proposed model is tested on the OULAD dataset that contains demographic, engagement, and assessment-related information. The pre-processing techniques are applied to handle missing values, normalize numerical attributes, and balance class distributions. The experimental results demonstrate that the proposed LSTM-SHAP model outperforms traditional machine learning methods in predicting student performance, achieving an accuracy of 92.88%, precision of 92.96%, recall of 92.80%, and F1-score of 92.79% on the OULAD dataset. In a cross-domain evaluation, it maintains strong generalizability with 85.0% accuracy, 98.4% precision, 78.5% recall, and an F1-score of 87.2% when tested on the xAPI dataset. Additionally, SHAP analysis reveals that assessment scores, time spent on course materials, and forum interactions are the most important factors behind academic success. The statistical tests, such as ANOVA, Friedman tests and post hoc Nemenyi, are applied to assess the statistical significance of the differences in performance between the models, and results suggest that LSTM outperforms other models in each performance metric.

Immersive learning has been recognized as a promising paradigm for enhancing educational experiences through the integration of VR. We propose an architecture for intelligent tutoring in immersive VR environments that employs LLM-based non-playable characters. Key system capabilities are identified, including natural language understanding, real-time adaptive dialogue, and multimodal interaction through hand tracking, gaze detection, and haptic feedback. The system synchronizes speech output with NPC animations, enhancing both interactional realism and cognitive immersion. This design demonstrates that AI-driven VR interactions can significantly improve learner engagement. System performance was generally stable; however, minor latency was observed during speech processing, indicating areas for technical refinement. Overall, this research highlights the transformative potential of VR in education and emphasizes the importance of ongoing optimization to maximize its effectiveness in immersive learning contexts.

Recently, using videos as a learning resource has received a lot of attention and turned widely exploited as an effective learning tool. With the rapid spread of instructional videos, the number of these tools in all disciplines is becoming very high. It is fractious for learners to find video courses adapted to their needs. Recommender system is an important tool; it can help learners for better navigation and better guidance to access and reach information and interesting videos content and restrict the number of choices provided. This article presents results of a systematic literature review on video-based learning recommender systems. This systematic literature review included the papers indexed in five scientific digital libraries and published between 2009 and 2023. Our review focuses on examining both the technical and pedagogical aspects of video-based learning recommender systems. In terms of technical aspects, we analyze the most commonly used recommendation approaches in video-based learning, as well as additional methods and techniques employed to enhance the effectiveness of video-based learning recommendations. We also explore the variables and data sources that have been investigated in the process of recommending videos for learning purposes, along with the evaluation metrics used to assess the performance of these systems. Regarding the pedagogical aspects, we investigate the learning environments where these systems have been implemented, the subjects and educational programs that hold primary importance in video-based learning recommendations, and the overall learning process.