Student learning progress as predictor for graduate employability performance

Wan Nor Afiqah, Wan Othman and Aziman, Abdullah (2020) Student learning progress as predictor for graduate employability performance. In: IOP Conference Series: Materials Science and Engineering; 6th International Conference on Software Engineering and Computer Systems, ICSECS 2019, 25 - 27 September 2019 , Vistana Kuantan City Center, Kuantan, Pahang. pp. 1-9., 769 (1). ISSN 1757-8981 (Print), 1757-899X (Online)

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Graduate employability is a major concern for higher education industry. There is a lack of research on the use of program learning outcomes (PLO) data to predict graduate employability performance especially on the duration they get employed. Therefore, our motivation in this study is to investigate how PLO data can be used to predict graduate employability performance. This study adopted quantitative analysis as a research method by using Simple Linear Regression to measure the highest correlation and significance values between learning progress and duration graduate to get employed. The PLO data from all semesters were segmented into four-time segments: 1st SEM, MID SEM, Pre-LI and LI. The slope value of linear model from time series analysis of four-time segments is used as a value to determine the performance of student learning progress. 47 responses (22% response rate) from 216 graduates who completed their study from Faculty of Computing, Universiti Malaysia Pahang in 2018 has been received as a case study. We found that learning progress from PLO 3 and PLO 6 which are 'Social Skills and Responsibilities' and 'Problem Solving and Scientific Skills' respectively, show significant values on the duration to get employed. This study highlights student learning progress is potential to be used as a predictor for graduate employability performance.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Higher education; Program learning outcomes; Graduate employability performance; Student learning progress
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 23 Aug 2022 07:57
Last Modified: 23 Aug 2022 07:57
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