Johnston, Laura J;
Jendoubi, Takoua;
(2024)
Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics.
In:
2024 IEEE Global Engineering Education Conference.
IEEE
(In press).
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Abstract
In the context of higher education's evolving dynamics post-COVID-19, this paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London. We employ a mixed methods approach, combining learning analytics with qualitative data, to evaluate the effectiveness of these incentives on increasing student engagement. A longitudinal overview of resource interactions is mapped through Bayesian network analysis of Moodle activity logs from 204 students. This analysis identifies early resource engagement as a predictive indicator of continued engagement while also suggesting that the new incentives disproportionately benefit highly engaged students. Focus group discussions complement this analysis, providing insights into student perceptions of the pedagogical changes and the module design. These qualitative findings underscore the challenge of sustaining engagement through the new incentives and highlight the importance of communication in blended learning environments. Our paper introduces an interpretable and actionable model for student engagement, which integrates objective, data-driven analysis with students' perspectives. This model provides educators with a tool to evaluate and improve instructional strategies. By demonstrating the effectiveness of our mixed methods approach in capturing the intricacies of student behaviour in digital learning environments, we underscore the model's potential to improve online pedagogical practices across diverse educational settings.
| Type: | Proceedings paper |
|---|---|
| Title: | Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics |
| Event: | 15th IEEE Global Engineering Education Conference (IEEE EDUCON) |
| Location: | GREECE |
| Dates: | 8 May 2024 - 11 May 2024 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/EDUCON60312.2024.10578692 |
| Publisher version: | http://dx.doi.org/10.1109/educon60312.2024.1057869... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Computer Science, computer science education, Computer Science, Interdisciplinary Applications, curriculum development, DYNAMIC BAYESIAN NETWORKS, Education & Educational Research, Education, Scientific Disciplines, educational technology, Engineering, Engineering, Multidisciplinary, learning management systems, ONLINE, Science & Technology, Social Sciences, statistical learning, Technology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10199285 |
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