Analytic Study for Predictor Development on Student Participation in Generic Competence Development Activities Based on Academic Performance
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| Title: | Analytic Study for Predictor Development on Student Participation in Generic Competence Development Activities Based on Academic Performance |
|---|---|
| Language: | English |
| Authors: | So, Joseph Chi-Ho (ORCID |
| Source: | IEEE Transactions on Learning Technologies. 2023 16(5):790-803. |
| Availability: | Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076 |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Predictor Variables, Higher Education, Online Courses, Correlation, Academic Achievement, Learning Analytics, Student Behavior, Artificial Intelligence, Algorithms, Holistic Approach, Competency Based Education, Learning Activities |
| DOI: | 10.1109/TLT.2023.3291310 |
| ISSN: | 1939-1382 |
| Abstract: | Generic competence (GC) development is an integral part of higher education to provide holistic education and enhance student career development. It also plays a critical role in complementing the curriculum. Many tertiary institutions provide various GC development activities (GCDA). Moreover, institutions strongly need to further understand student participation, especially its relationship to student backgrounds, activity profiles, and academic results. With the fast advancement of educational technologies and data mining, data analytics (DA) in formal learning and online education has been widely explored. However, there has been little work on student behavior in GCDA. To fill this gap and to provide new contributions, we conduct a comprehensive study to investigate the interrelationship of GCDA participation and academic performance before and after higher education with significant and representative data (over 10 000 records) across three years. Hypotheses are formulated and validated, and the findings are triangulated with machine learning (ML) and DA. With supervised learning, the predictors of academic performance and GCDA participation are formulated, and the features to enhance predictions are analyzed. We develop predictors using novel approaches of genetic algorithms and Stacking in ML. The impacts of the breadth and depth of involvement are also studied. Results indicate that involvement in GCDA positively impacts student academic results. Our novel approaches give improvements in predicting student participation. Our holistic studies covering hypothesis validation, data analysis, and ML provide valuable insights into GCDA development. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1396454 |
| Database: | ERIC |
| Abstract: | Generic competence (GC) development is an integral part of higher education to provide holistic education and enhance student career development. It also plays a critical role in complementing the curriculum. Many tertiary institutions provide various GC development activities (GCDA). Moreover, institutions strongly need to further understand student participation, especially its relationship to student backgrounds, activity profiles, and academic results. With the fast advancement of educational technologies and data mining, data analytics (DA) in formal learning and online education has been widely explored. However, there has been little work on student behavior in GCDA. To fill this gap and to provide new contributions, we conduct a comprehensive study to investigate the interrelationship of GCDA participation and academic performance before and after higher education with significant and representative data (over 10 000 records) across three years. Hypotheses are formulated and validated, and the findings are triangulated with machine learning (ML) and DA. With supervised learning, the predictors of academic performance and GCDA participation are formulated, and the features to enhance predictions are analyzed. We develop predictors using novel approaches of genetic algorithms and Stacking in ML. The impacts of the breadth and depth of involvement are also studied. Results indicate that involvement in GCDA positively impacts student academic results. Our novel approaches give improvements in predicting student participation. Our holistic studies covering hypothesis validation, data analysis, and ML provide valuable insights into GCDA development. |
|---|---|
| ISSN: | 1939-1382 |
| DOI: | 10.1109/TLT.2023.3291310 |
