Work

A novel student dataset for ML based effective career growth recommendation

Year: 2026

Type: article

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.

Source: Scientific Reports

DOI: 10.1038/s41598-026-58086-w

Authors: Savitha Acharya, Surendra Shetty, Niranjan N. Prabhu, Nagaraja Shetty

Institutions: Nitte University, Visvesvaraya Technological University, Manipal Academy of Higher Education

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,690