Academic Recommendation Framework with Temporal Dynamic Pattern Analysis Using FSPN-ADGAT for Indian Higher Education Institutions

Recently, the academic recommendation system represents the process of suggesting suitable institutions, courses, or learning pathways for students based on their performances and interests. Yet, the conventional systems didn’t concentrate on temporal dynamic pattern analysis within the Indian higher education institutions, leading to less effective or static academic recommendations. Thus, an academic recommendation system is proposed for Indian higher education institutions using Few-Shot PairNorm-Apical Dendrite Graph Attention Networks (FSPN-ADGAT) by considering temporal dynamic pattern analysis. Primarily, the student data undergoes pre-processing. Further, student performance analysis is done, followed by feature extraction. Now, the institutional course data undergoes pre-processing, followed by contextual embedding of text using Adapter Layers-Bidirectional Encoder Representations from Transformers (AL-BERT). Similarly, by using SRC, course similarity is analyzed between the pre-processed course data and extracted features. Similarly, the temporal dynamic pattern analysis is done from the pre-processed course data using Student-t Likelihood-based Bayesian Change Point (SL-BCP) and indicator extraction. Now, based on the analyzed course similarity, extracted features, contextual embedding output, analyzed temporal dynamic patterns, and extracted indicators, the node and matrix construction is performed. Lastly, the academic recommendation using FSPN-ADGAT provides personalized course suggestions to the students. Therefore, the proposed FSPN-ADGAT attained a lower Mean Absolute Error (MAE) of 0.171 than the conventional techniques.