eprintid: 10199285 rev_number: 6 eprint_status: archive userid: 699 dir: disk0/10/19/92/85 datestamp: 2024-10-31 16:30:15 lastmod: 2024-10-31 16:30:15 status_changed: 2024-10-31 16:30:15 type: proceedings_section metadata_visibility: show sword_depositor: 699 creators_name: Johnston, Laura J creators_name: Jendoubi, Takoua title: Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics ispublished: inpress divisions: UCL divisions: B04 divisions: C06 divisions: F61 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 note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. 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. date: 2024-07-08 date_type: published publisher: IEEE official_url: http://dx.doi.org/10.1109/educon60312.2024.10578692 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2303029 doi: 10.1109/EDUCON60312.2024.10578692 lyricists_name: Jendoubi, Takoua lyricists_id: TJEND13 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: public pres_type: paper publication: 2024 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE, EDUCON 2024 pages: 5 event_title: 15th IEEE Global Engineering Education Conference (IEEE EDUCON) event_location: GREECE event_dates: 8 May 2024 - 11 May 2024 book_title: 2024 IEEE Global Engineering Education Conference citation: 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). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10199285/1/2403.14686v1.pdf