Students’ complex trajectories: exploring degree change and time to degree
Abstract The complex trajectories of higher education students are deviations from the regular path due to delays in completing a degree, dropping out, taking breaks, or changing programmes. In this study, we investigated degree changing as a cause of complex student trajectories. We characterised c...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-01-01
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| Series: | International Journal of Educational Technology in Higher Education |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41239-024-00438-5 |
| _version_ | 1797273573641945088 |
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| author | João Pedro Pêgo Vera Lucia Miguéis Alfredo Soeiro |
| author_facet | João Pedro Pêgo Vera Lucia Miguéis Alfredo Soeiro |
| author_sort | João Pedro Pêgo |
| collection | DOAJ |
| description | Abstract The complex trajectories of higher education students are deviations from the regular path due to delays in completing a degree, dropping out, taking breaks, or changing programmes. In this study, we investigated degree changing as a cause of complex student trajectories. We characterised cohorts of students who graduated with a complex trajectory and identified the characteristics that influenced the time to graduation. To support this predictive task, we employed machine learning techniques such as neural networks, support vector machines, and random forests. In addition, we used interpretable techniques such as decision trees to derive managerial insights that could prove useful to decision-makers. We validated the proposed methodology taking the University of Porto (Portugal) as case study. The results show that the time to degree (TTD) of students with and without complex trajectories was different. Moreover, the proposed models effectively predicted TTD, outperforming two benchmark models. The random forest model proved to be the best predictor. Finally, this study shows that the factors that best predict TTD are the median TTD and the admission regime of the programme of destination of transfer students, followed by the admission average of the previous programme. By identifying students who take longer to complete their studies, targeted interventions such as counselling and tutoring can be promoted, potentially improving completion rates and educational outcomes without having to use as many resources. |
| first_indexed | 2024-03-07T14:46:20Z |
| format | Article |
| id | doaj.art-0f81f5f6731e4d35b1dd61164b1b1107 |
| institution | Directory Open Access Journal |
| issn | 2365-9440 |
| language | English |
| last_indexed | 2024-03-07T14:46:20Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Educational Technology in Higher Education |
| spelling | doaj.art-0f81f5f6731e4d35b1dd61164b1b11072024-03-05T19:56:30ZengSpringerOpenInternational Journal of Educational Technology in Higher Education2365-94402024-01-0121112910.1186/s41239-024-00438-5Students’ complex trajectories: exploring degree change and time to degreeJoão Pedro Pêgo0Vera Lucia Miguéis1Alfredo Soeiro2Faculdade de Engenharia, da Universidade do Porto, Departamento de Engenharia CivilFaculdade de Engenharia da Universidade do Porto, INESC TECFaculdade de Engenharia da Universidade do PortoAbstract The complex trajectories of higher education students are deviations from the regular path due to delays in completing a degree, dropping out, taking breaks, or changing programmes. In this study, we investigated degree changing as a cause of complex student trajectories. We characterised cohorts of students who graduated with a complex trajectory and identified the characteristics that influenced the time to graduation. To support this predictive task, we employed machine learning techniques such as neural networks, support vector machines, and random forests. In addition, we used interpretable techniques such as decision trees to derive managerial insights that could prove useful to decision-makers. We validated the proposed methodology taking the University of Porto (Portugal) as case study. The results show that the time to degree (TTD) of students with and without complex trajectories was different. Moreover, the proposed models effectively predicted TTD, outperforming two benchmark models. The random forest model proved to be the best predictor. Finally, this study shows that the factors that best predict TTD are the median TTD and the admission regime of the programme of destination of transfer students, followed by the admission average of the previous programme. By identifying students who take longer to complete their studies, targeted interventions such as counselling and tutoring can be promoted, potentially improving completion rates and educational outcomes without having to use as many resources.https://doi.org/10.1186/s41239-024-00438-5Complex trajectoriesMachine learning modelTime to degreeProgramme transfer |
| spellingShingle | João Pedro Pêgo Vera Lucia Miguéis Alfredo Soeiro Students’ complex trajectories: exploring degree change and time to degree International Journal of Educational Technology in Higher Education Complex trajectories Machine learning model Time to degree Programme transfer |
| title | Students’ complex trajectories: exploring degree change and time to degree |
| title_full | Students’ complex trajectories: exploring degree change and time to degree |
| title_fullStr | Students’ complex trajectories: exploring degree change and time to degree |
| title_full_unstemmed | Students’ complex trajectories: exploring degree change and time to degree |
| title_short | Students’ complex trajectories: exploring degree change and time to degree |
| title_sort | students complex trajectories exploring degree change and time to degree |
| topic | Complex trajectories Machine learning model Time to degree Programme transfer |
| url | https://doi.org/10.1186/s41239-024-00438-5 |
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