Citation

Mahdin, Hairulnizam and Nurwarsito, Heru and Baharum, Zirawani and Kamri, Khairol Anuar and Hassan, Azman and Haw, Su Cheng and Arshad, Mohammad Syafwan (2025) Predictive Analytics for Employability in Malaysian TVET with a Hybrid of Regression and Clustering Methods. JOIV : International Journal on Informatics Visualization, 9 (5). p. 1816. ISSN 2549-9610

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Abstract

Graduate employability remains a high concern for Technical and Vocational Education and Training (TVET) institutions, particularly within Malaysia’s Technical University Network (MTUN), where producing industry-ready graduates is a central goal. While machine learning has transformed fields like healthcare and finance, its application in vocational education remains underexplored—particularly for employability prediction. This study addresses this gap by hybridizing decision trees and clustering to uncover non-linear patterns in student survey data. Guided by Human Capital Theory and SERVQUAL, which inform variable selection (e.g., technical skills as productivity investments), this study integrates multiple linear regression, decision tree regression, and K-Means clustering to identify significant predictors and uncover latent student groupings. Using a publicly available dataset of Likert-scale responses from MTUN students, technical skills and supervisory support consistently emerged as the most impactful employability predictors. Communication showed moderate influence, while training delivery and problem-solving exhibited variable effects depending on the modelling approach. Unlike regression, decision trees revealed non-linear interaction thresholds. For example, students with SVR < 3.5 and TS < 4.0 had 40% lower employability scores, suggesting targeted mentoring could yield disproportionate improvements. Clustering revealed three distinct student profiles, which could support data-driven interventions. This hybrid framework demonstrates the potential for integrating machine learning into institutional analytics for proactive support of employability.

Item Type:	Article
Uncontrolled Keywords:	Predictive analytics, decision tree regression, K-Means clustering, human capital theory, SERVQUAL
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.23 Decision making
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	10 Dec 2025 07:14
Last Modified:	13 Dec 2025 08:32
URII:	http://shdl.mmu.edu.my/id/eprint/15034

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