Open AccessStudent Learning Progress as Predictor for Graduate Employability Performance
Author(s) -
Wan Nor Afiqah Wan Othman,
Abdul Adam Abdullah
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/769/1/012019
Subject(s) - employability , duration (music) , graduate students , regression analysis , mathematics education , psychology , value (mathematics) , medical education , computer science , pedagogy , machine learning , medicine , art , literature
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: 1 st 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.