Abstract

Explainable Artificial Intelligence (XAI) has become essential for trustworthy and clear decision-making in educational analytics. Traditional learning analytics systems often depend on single data sources and unclear models, which limits understanding and practical use in higher education. This publication introduces an explainable AI framework for multimodal learning analytics that combines various educational data, including academic records, learning management system logs, behavioral indicators, and assessment outcomes. The proposed framework uses multimodal representation learning and explanation techniques to provide understandable insights into student performance, engagement, and learning outcomes. By merging predictive accuracy with clarity, the framework aids data-driven academic decision-making while building trust among stakeholders. Tests with representative educational datasets show that this approach effectively improves understanding without sacrificing analytical performance. The framework is scalable, flexible, and suitable for real-world use in higher education institutions.

Problem Statement

Higher education institutions produce large amounts of diverse data from many sources, including academic systems, learning platforms, and student interactions. Current learning analytics solutions often handle this data separately and use black-box models, making it hard for educators and administrators to understand, trust, and act on the results. There is a pressing need for a unified, explainable framework that can analyze multimodal educational data while offering clear and actionable insights.

Proposed Methodology

The proposed framework employs a multimodal learning architecture that combines various educational data sources into a single analytical process. Feature representations from different sources are learned separately and fused using attention-based methods to capture relationships between them. Explainability is built in using both model-agnostic and model-specific XAI techniques that highlight the key factors influencing predictions. This design ensures high predictive performance and human-readable explanations suitable for academic stakeholders.

Evaluation and Results

The framework is assessed with representative higher education datasets using standard performance metrics and explainability evaluations. Results show that the multimodal approach outperforms single-source baselines in predictive accuracy while greatly improving understanding. Visual and quantitative explanations help educators better grasp student learning patterns and associated risk factors.

Impact and Applications

The proposed explainable multimodal learning analytics framework encourages clear decision-making in areas like student performance prediction, early risk identification, curriculum evaluation, and institutional planning. By enhancing trust and usability, the framework promotes responsible AI use in higher education and meets ethical and regulatory standards.

Conclusion

This work demonstrates that combining explainability and multimodal learning can effectively tackle key challenges in educational analytics. The proposed framework offers a practical, scalable, and clear solution for higher education institutions aiming to responsibly use AI-driven insights.