An integrated model for digital student profiling based on multisource educational data

Introduction This paper proposes an integrated model of student digital profiling based on multisource educational data. The aim of the model is to improve the accuracy of predicting academic performance and learning risks. Unlike traditional approaches that rely on a limited set of academic indicators, the proposed model integrates academic, research, social, and behavioral characteristics to form a holistic representation of learning activity. Methods The methodological framework includes semantic clustering of disciplines using NLP methods, calculation of an integrated grade point average (IGPA), and the application of multimodal machine learning models. SHAP analysis was used to interpret the results. Experimental validation was conducted on real data from a university information system using the AUC-PR, LogLoss, KS, and PSI metrics. Results The results demonstrate that the use of multimodal data increases the accuracy of learning risk prediction by 8–12% compared with baseline models. Dynamic behavioral features enable the early identification of academic underperformance risks at initial stages of study. The model demonstrated robustness when applied across different faculties and scalability under conditions of increasing data volume. Discussion Practical implementation in the form of interactive dashboards confirmed the applicability of the proposed approach for supporting pedagogical and managerial decision-making. The model provides a foundation for personalized academic support and the development of digital educational analytics.