Use of Learner Engagement Analytics to empower medical educators to make data-informed decisions

Authors

  • Usman Naeem Queen Mary University of London https://orcid.org/0000-0001-5250-1390
  • Matthew Simmons Queen Mary University of London
  • Graeme Hathaway Queen Mary University of London
  • Lujain Alsadder Queen Mary University of London

DOI:

https://doi.org/10.47408/jldhe.vi32.1427

Keywords:

student engagement, virtual learning environment, medical education, curriculum design, learner engagement analytics

Abstract

Learner Engagement Analytics (LEA) has enabled Higher Education Institutions (HEIs) to identify learners who are not engaging with their studies and provide targeted support and help (Naeem and Bosman, 2023). It has also allowed educators to make data-informed decisions to inform their curriculum design and classroom practice (Cogliano et al., 2022). The LEA data is captured from a wide range of sources related to teaching and learning which offer meaningful insights into a learner’s learning habits (Eady et al., 2021). Previous research suggests that providing learning analytics to educators in Higher Education Institutions can improve learning outcomes for students (Aslan et al., 2019).

At Queen Mary University of London, a multidisciplinary team was formed of medical educators, LEA experts, learning technologists, and learning innovation professionals to investigate how LEA can inform curriculum redesign in the early phase of the medical curriculum. Through a series of scholarship meetings on LEA using data dashboards from the Virtual Learning Environment (VLE), the team analysed learners’ engagement with the virtual pre-sessional resources. The VLE interactive resources were designed to allow medical learners to develop clinical interpretation skills during practical sessions, as recommended by the General Medical Council (GMC, 2018). However, medical educators lacked the metrics to evaluate learners’ interaction with these virtual resources. Therefore, it was inevitable to train educators on how to use LEA to optimise students’ learning. The team assessed the learners' engagement on the VLE, quantified engagement scores, and evaluated the results against key outcomes, including the learners' performance. The LEA data offered further insights into virtual engagement across multiple modules in the medical curriculum. Effectively, the outcome of this work empowered medical educators to make informed decisions regarding the future use of VLE resources in curriculum design and develop virtual resources to increase students’ engagement and enhance their learning.

Author Biographies

Usman Naeem, Queen Mary University of London

Usman Naeem is a Reader (Associate Professor) in Computer Science Education at the School of Electronic Engineering and Computer Science at the Queen Mary University of London. Usman is also a Queen Mary Academy Fellow in Learner Engagement Analytics. His research focus is on educational and assistive technologies, which include machine learning techniques, mobile computing, and ambient intelligent environments. Usman has taught in various programmes, ranging from traditional programmes such as BSc Computer Science to degree apprenticeship programmes such as BSc Digital & Technology Solutions Professional. Usman is also a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).

Matthew Simmons, Queen Mary University of London

Matthew Simmons is an E-learning Technologist at the Institute of Health Sciences Education, Queen Mary University of London.

Graeme Hathaway, Queen Mary University of London

Graeme Hathaway is a Learning Innovation Manager at Queen Mary Academy, Queen Mary University of London.

Lujain Alsadder, Queen Mary University of London

Lujain Alsadder is Senior Lecturer in Physiology at the Institute of Health Sciences Education, Queen Mary University of London. Lujain is a member of the physiology team and teaches the Cardiorespiratory and Carriage of Oxygen modules in Year 1 and 2 MBBS programs, the Graduate Entry Program, and the Physician Associate Studies MSc program. She has a special interest in physiology education, digital accessibility, sustainability and AI in healthcare, and learner engagement. Lujain is an active member of The Physiological Society and the Association for The Study of Medical Education (ASME).

References

Aslan, S., Alyuz, N., Tanriover, C., Mete, S.E., Okur, E., D'mello, S.K. and Esme, A.A. (2019) ‘Investigating the impact of a real-time, multimodal student engagement analytics technology in authentic classrooms’, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Glasgow, Scotland UK: Association for Computing Machinery.

Brown, A., Lawrence, J., Basson, M. and Redmond, P. (2022) ‘A conceptual framework to enhance student online learning and engagement in higher education’, Higher Education Research & Development, 41(2), pp.284-299. Available at: https://doi.org/10.1080/07294360.2020.1860912

Cogliano, M., Bernacki, M. L., Hilpert, J. C. and Strong, C. L. (2022) ‘A self-regulated learning analytics prediction-and-intervention design: detecting and supporting struggling biology students’, Journal of Educational Psychology, 114(8), pp.1801-1816.

Eady, M.J., Green, C.A., Fulcher, D. and Boniface, T (2021) ‘Using learning analytics to redesign core pedagogy subjects: a case in point’, Journal of Further and Higher Education, 46(2), pp.246-257. Available at: https://doi.org/10.1080/0309877X.2021.1905156

GMC (2018). Outcomes for graduates. London: General Medical Council, 28. Available at: https://www.gmc-uk.org/-/media/documents/outcomes-for-graduates-2020_pdf-84622587.pdf (Accessed: 2 May 2024).

Li, J. and Xue, E. (2023) ‘Dynamic interaction between student learning behaviour and learning environment: meta-analysis of student engagement and its influencing factors’, Behavioral Sciences, 13(1). Available at: https://doi.org/10.3390/bs13010059

McLaughlin, J.E., Chen, E., Lake, D., Guo, W., Skywark, E.R., Chernik, A. and Liu, T. (2022) ‘Design thinking teaching and learning in higher education: experiences across four universities’, PLoS ONE, 17(3). Available at: https://doi.org/10.1371/journal.pone.0265902

Naeem, U. and Bosman, L. (2023) ‘Learner engagement analytics in a hybrid learning environment’, 2023 IEEE Global Engineering Education Conference (EDUCON 2023). Kuwait, pp.1-7. Available at: https://doi.org/10.1109/EDUCON54358.2023.10125108

Shao, S. (2023) ‘CRUD in enterprise UX… and what’s more?’, Medium, 27 September. Available from: https://bootcamp.uxdesign.cc/crud-in-enterprise-application-and-whats-more-b522bded803d (Accessed: 22 July 2024).

Veluvali, P. and Surisetti, J. (2022) ‘Learning management system for greater learner engagement in higher education – A review’, Higher Education for the Future, 9(1), pp.107-121. Available at: https://doi.org/10.1177/23476311211049855

Whelehan, D. F. (2020) ‘Students as partners: a model to promote student engagement in post-COVID-19 teaching and learning’, All Ireland Journal of Higher Education, 12(3).

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Published

31-10-2024

How to Cite

Naeem, U. (2024) “Use of Learner Engagement Analytics to empower medical educators to make data-informed decisions ”, Journal of Learning Development in Higher Education, (32). doi: 10.47408/jldhe.vi32.1427.

Issue

No. 32 (2024): Special Edition, ALDinHE Conference Proceedings and Reflections

Section

Conference Proceedings

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