Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jayaraj, Jishnu Plavinchottil; Ashok Immanuel, V.; Chandra, J. Title A name given to the resource Personalized Explainable Transformer Models for Student Performance Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.44-49 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00026" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00026</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037468909?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037468909?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jayaraj J.P., CHRIST University, Department of Computer Science, Bangalore, India; Ashok Immanuel V., CHRIST University, Department of Computer Science, Bangalore, India; Chandra J., CHRIST University, Department of Computer Science, Bangalore, India Description An account of the resource The research presents a unified framework for forecasting student academic achievement using a transformer-based architecture supported by Explainable Artificial Intelligence techniques. The research is motivated by the need to combine predictive accuracy of transformer models with interpretability in student performance prediction. The framework applies an adapted Feature Tokenizer Transformer to the UCI Student Performance dataset and integrates SHAP and LIME methods to generate instance level, human readable explanations for each prediction. These explanations can help educators design targeted interventions. A Random Forest regressor is included as a baseline for comparison. The experiment results showed that the Random Forest performed slightly better than the Feature Tokenizer Transformer, which could be due to the small size of the dataset and certain features having a strong impact on the results. Nevertheless, the results show that modern deep learning models combined with personalized explainability offer a practical foundation for scalable solutions in more complex educational datasets, which helps connect high performing prediction models with actionable insights, contributing to the development of interpretable, data driven student support systems. 2025 IEEE. Subject The topic of the resource Explainable AI; LIME; SHAP; Student Performance Prediction; Transformer Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online