SMART EDUCATIONAL APP SELECTION THROUGH INTELLIGENT RECOMMENDATIONS

Abstract

Personalised recommendations are essential for improving students' educational experiences in the quickly changing digital age. The growing use of mobile devices has made it simpler to gather app usage data, which may be used to offer customised recommendations for educational apps. The goal of this study is to recommend appropriate apps for college students based on their app usage habits, with an emphasis on Undergraduate (UG), Postgraduate (PG), and Graduate levels. In order to extract pertinent characteristics from app descriptions and student interaction data, the dataset is pre-processed using Natural Language Processing (NLP) techniques such as text cleaning, tokenisation, and feature extraction. Students' tastes are precisely categorised by the algorithm, which then suggests apps that meet their academic demands.