A novel student dataset for ML based effective career growth recommendation

Abstract

Abstract Educational Data Mining (EDM) techniques are increasingly employed to analyze student data for predicting optimal career paths and providing tailored recommendations. A major challenge, however, is the lack of a benchmark dataset that effectively supports this objective, along with the difficulty of identifying the most relevant student attributes for career growth decision support. This study addresses the need for a comprehensive and well-structured dataset to facilitate research on personalized career growth recommendations for engineering students. It presents the methodology used to curate and preprocess a novel benchmark dataset encompassing student demographics, academic background, technical and soft skills, and stress-related factors. Challenges such as data heterogeneity, sparsity, and noise were managed through rigorous data cleaning, feature engineering, and dimensionality reduction techniques.