Predicting Student's Soft Skills Based on Socio-Economical Factors: An Educational Data Mining Approach


Kannan, Rathimala and Chew, Chin Jet and Ramakrishnan, Kannan and Ramdass, Sujatha (2023) Predicting Student's Soft Skills Based on Socio-Economical Factors: An Educational Data Mining Approach. JOIV : International Journal on Informatics Visualization, 7 (3-2). p. 2040. ISSN 2549-9610

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Recent changes in the labor market and higher education sector have made graduates' employability a priority for researchers, governments, and employers in developed and emerging nations. There is, however, still a dearth of study about whether graduate students acquire the employability skills that businesses want of them because of their higher education. To determine a student's future employment and career path, it is critical to evaluate their soft skills. An emerging area called educational data mining (EDM) aims to gather enormous volumes of academic data produced and maintained by educational institutions and to derive explicit and specific information from it. This paper aims to predict students' soft skills such as professional, analytical, linguistic, communication, and ethical skills, based on their socio-economic, academic, and institutional data by leveraging data mining methods and machine learning techniques. All five soft skills were predicted using prediction models created using linear regression, probabilistic neural networks, and simple regression tree techniques. This study used a dataset from an open source that Universidad Technologica de Bolivar published. It covers academic, social, and economic data for 12,411 students. The experimental results demonstrated that the linear regression algorithm performed better than the others in predicting all five soft skills compared to machine learning methods. This finding can assist higher education institutions in making informed decisions, providing tailored support, enhancing student success and employability, and continuously modifying their programs to meet the needs of students

Item Type: Article
Uncontrolled Keywords: Prediction, machine learning, regression models, soft skills, higher education institutes
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions: Faculty of Management (FOM)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 27 Mar 2024 02:46
Last Modified: 27 Mar 2024 02:46


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