Title: Prescriptive data analytics to foster student employability in engineering education

Authors: Suja Jayachandran; Bharti Joshi

Addresses: Department of Computer Engineering, Ramrao Adik Institute of Technology, D.Y. Patil Deemed to be University, Nerul, Navi Mumbai, Maharashtra, India; Department of Computer Engineering, Vidyalankar Institute of Technology, Wadala, Mumbai, Maharashtra, India ' Department of Computer Engineering, Ramrao Adik Institute of Technology, D.Y. Patil Deemed to be University, Nerul, Navi Mumbai, Maharashtra, India

Abstract: Higher education is vital to a country's economy because it creates a trained and steady labour force. Consequently, there is pressure on academic institutions to come up with innovative strategies to make their students more employable. To generate commercial value, business analytics empowers organisations to make better decisions more quickly, and intelligently. As of now, descriptive, and predictive analytics are the main areas of interest for both academia and business. In this research, we analysed 2694 engineering graduates who graduated between 2018 and 2022 from a cosmopolitan city located in India and performed descriptive, predictive, and prescriptive data analytics. We have used a hybrid feature selection algorithm to find an optimal set of features that has the maximum influence on employability and evaluated it using the classifier. We used these results and with the help of the 5W1H methodology, prescribed strategies to foster employability among engineering graduates.

Keywords: prescriptive analytics; student employability; engineering institute; machine learning model; 5W1H model.

DOI: 10.1504/IJMIE.2025.144722

International Journal of Management in Education, 2025 Vol.19 No.2, pp.141 - 159

Received: 12 Jan 2024
Accepted: 19 Apr 2024
Published online: 28 Feb 2025 *

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