Predicting graduate employability based on program learning outcomes
Many studies based on the literature and adopted approach by Ministry of Higher Education regarding graduate employability are using survey. This approach is lack with on-demand analytical capability for impactful decision making. There is a lack of study that predicts the duration of graduate to ge...
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| Fformat: | Conference or Workshop Item |
| Iaith: | English |
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IOP Publishing
2020
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| Pynciau: | |
| Mynediad Ar-lein: | http://umpir.ump.edu.my/id/eprint/29168/1/Predicting%20graduate%20employability%20based%20on%20program%20learning.pdf |
| _version_ | 1825813476832968704 |
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| author | Wan Nor Afiqah, Wan Othman Aziman, Abdullah Awanis, Romli |
| author_facet | Wan Nor Afiqah, Wan Othman Aziman, Abdullah Awanis, Romli |
| author_sort | Wan Nor Afiqah, Wan Othman |
| collection | UMP |
| description | Many studies based on the literature and adopted approach by Ministry of Higher Education regarding graduate employability are using survey. This approach is lack with on-demand analytical capability for impactful decision making. There is a lack of study that predicts the duration of graduate to get employed based on quantitative analysis. Since all institutions of higher education are compulsory to adopt and implement outcomes-based education (OBE), this study aims to develop a predictive model on GE based on program learning outcomes (PLO) data. There are two data sources used in this study, institutional academic database and online feedback from graduate. This study used simple linear regression to measure the degree of relationship between the category of PLO with the duration of graduate to get employed. This study received 47 responses from 216 with a response rate of 22%. PLO1 and PLO6 which are 'knowledge' and 'problem solving and scientific skills' respectively show high significance values on the duration of graduate to get employed. The linear models developed based on PLO1 and PLO6 were validated with error rate analysis and evaluated with error rate frequency analysis. The results show the model has potential value to be used to predict graduate employability performance within the time frame (6 months) as determined by Ministry of Higher Education. With prediction capacity from the developed model, more intervention program can be strategically planned to assure graduate can be employed in time and in-field. |
| first_indexed | 2024-03-06T12:44:35Z |
| format | Conference or Workshop Item |
| id | UMPir29168 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:44:35Z |
| publishDate | 2020 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | UMPir291682022-04-22T02:06:15Z http://umpir.ump.edu.my/id/eprint/29168/ Predicting graduate employability based on program learning outcomes Wan Nor Afiqah, Wan Othman Aziman, Abdullah Awanis, Romli LB2300 Higher Education QA Mathematics QA76 Computer software Many studies based on the literature and adopted approach by Ministry of Higher Education regarding graduate employability are using survey. This approach is lack with on-demand analytical capability for impactful decision making. There is a lack of study that predicts the duration of graduate to get employed based on quantitative analysis. Since all institutions of higher education are compulsory to adopt and implement outcomes-based education (OBE), this study aims to develop a predictive model on GE based on program learning outcomes (PLO) data. There are two data sources used in this study, institutional academic database and online feedback from graduate. This study used simple linear regression to measure the degree of relationship between the category of PLO with the duration of graduate to get employed. This study received 47 responses from 216 with a response rate of 22%. PLO1 and PLO6 which are 'knowledge' and 'problem solving and scientific skills' respectively show high significance values on the duration of graduate to get employed. The linear models developed based on PLO1 and PLO6 were validated with error rate analysis and evaluated with error rate frequency analysis. The results show the model has potential value to be used to predict graduate employability performance within the time frame (6 months) as determined by Ministry of Higher Education. With prediction capacity from the developed model, more intervention program can be strategically planned to assure graduate can be employed in time and in-field. IOP Publishing 2020-06-05 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/29168/1/Predicting%20graduate%20employability%20based%20on%20program%20learning.pdf Wan Nor Afiqah, Wan Othman and Aziman, Abdullah and Awanis, Romli (2020) Predicting graduate employability based on program learning outcomes. 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) (Published) https://doi.org/10.1088/1757-899X/769/1/012018 |
| spellingShingle | LB2300 Higher Education QA Mathematics QA76 Computer software Wan Nor Afiqah, Wan Othman Aziman, Abdullah Awanis, Romli Predicting graduate employability based on program learning outcomes |
| title | Predicting graduate employability based on program learning outcomes |
| title_full | Predicting graduate employability based on program learning outcomes |
| title_fullStr | Predicting graduate employability based on program learning outcomes |
| title_full_unstemmed | Predicting graduate employability based on program learning outcomes |
| title_short | Predicting graduate employability based on program learning outcomes |
| title_sort | predicting graduate employability based on program learning outcomes |
| topic | LB2300 Higher Education QA Mathematics QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/29168/1/Predicting%20graduate%20employability%20based%20on%20program%20learning.pdf |
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