Exploring the dimensions of medical student engagement with technology-enhanced learning resources and assessing the impact on assessment outcomes
Abstract
Anatomy curricula are becoming increasingly populated with blended learning resources, which utilize the increasing availability of educational technology. The educational literature postulates that the use of technology can support students in achieving greater learning outcomes by increasing engagement. This study attempts to investigate the dimensions of student engagement with technology‐enhanced learning (TEL) resources as part of a medical program’s anatomy curriculum using exploratory factor analysis. A 25‐item five‐point Likert‐based survey was administered to 192 first‐year medical students, with three emergent factors discerned: satisfaction, goal setting and planning, and physical interaction. The three factors closely aligned with the existing literature and therefore additional nonparametric analysis was conducted that explored the levels of engagement across three custom‐made anatomy TEL resources, including: (1) anatomy drawing screencasts; (2) an eBook; and (3) a massive open online course (MOOC). Usage data indicated that the most popular resource to be accessed across the cohort was the anatomy drawing screencasts via YouTube, with the MOOC being used least. Moreover, some evidence suggests that those students who utilized the MOOC were more engaged. Generally, however, no correlations were observed between the levels of engagement and TEL resource usage or assessment outcomes. The results from this study provide a clear insight into how students engage with TEL resources, but do not reveal any relationship between levels of engagement, usage, and assessment outcomes.
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Learner satisfaction in massively developed online courses tends to be lower than in traditional offline classrooms.
“learner satisfaction in massively developed online courses tends to be lower than in traditional offline classrooms (Pickering and Swinnerton, 2019)”
From Exploring the factors influencing college students’ learning satisfaction in generative AI-supported MOOCs learning environment: a learning experience framework perspective (2025) · cites this paper as Pickering and Swinnerton, 2019
The ‘Interplay between engagement, usage and assessment outcomes’ section says that a subset of students who used the MOOC scored highly on a satisfaction factor. It does not compare their satisfaction with that of students in traditional offline classrooms. Searches for an online-versus-offline satisfaction comparison found no such passage in our copy. (LLM, conf=0.76)
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References 87 · Cited by 118
Research works printed but not linked: 8
- Attardi SM, Barbeau ML, Rogers KA. 2018. Improving online interactions: Lessons from an online anatomy course with a laboratory for undergraduate students. Anat Sci Educ (in press; doi: 10.1002/ase.1776). · DOI
- Chan D. 2009. So why ask me? Are self-report data really that bad? In: Lance C, Vandenberg R (Editors). Statistical and Methodological Myths and Urban Legends: Doctrine, Verity and Fable in Organizational and Social Sciences. 1 st Ed. New York, NY: Routledge. p 309 -336.
- Field A. 2009. Discovering Statistics Using SPSS. 3rd Ed. London, UK: SAGE Publications Ltd. 854 p.
- Green RA, Whitburn LY, Zacharias A, Byrne G, Hughes DL. 2018. The relationship between student engagement with online content and achievement in a blended learning anatomy course. Anat Sci Educ (in press; doi: 10.1002/ase.1761). · DOI
- Kirkwood A, Price L. 2014. Technology-enhanced learning and teaching in higher education: what is 'enhanced' and how do we know? A critical literature review. Learn Media Tech 39:6 -36.
- Liu Q, Peng W, Zhang F, Hu R, Li Y, Yan W. 2016. The effectiveness of blended learning in health professions: Systematic review and meta-analysis. J Med Internet Res 18:e2.
- Trowler V. 2010. Student Engagement Literature Review. 1 st Ed. Heslington, York, UK: The Higher Education Academy. 70 p.URL:
- Wilson AB, Brown KM, Misch J, Miller CH, Klein BA, Taylor MA, Goodwin M, Boyle EK, Hoppe C, Lazarus MD. 2018. Breaking with tradition: A scoping meta-analysis analyzing the effects of student-centered learning and computer-aided instruction on student performance in anatomy. Anat Sci Educ (in press; doi: 10.1002/ase.1789). · DOI
- Authors
- Pickering, J. D., & Swinnerton, B. J.
- Affiliations
- University of Leeds (GB)
- Corresponding author
- Pickering, James D.
- Venue
- Anatomical Sciences Education
- Publisher
- Wiley
- Published
- 2018-08-31
- Volume
- 12
- Issue
- 2
- Pages
- 117-128
- ISSN
- 1935-9772 (ISSN-L)
- 1935-9780
- OpenAlex
- W2888936411
- Cited by
- 118
- References
- 87
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 12 · 2023: 19 · 2022: 18 · 2021: 24 · 2020: 16 · 2019: 8
- FWCI
- 4.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Anatomical Sciences Education (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Funders
- University of Leeds
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Social Sciences › Education › Online and Blended Learning
- Keywords
- student engagement, technology-enhanced learning, anatomy education, medical students, blended learning, screencasts, massive open online courses, e-books, student satisfaction, goal setting, physical interaction, evaluation results, resource utilization
- MeSH terms
- Anatomy, Computer-Assisted Instruction, Curriculum, Education, Medical, Undergraduate, Educational Measurement, Female, Humans, Male, Surveys and Questionnaires, Students, Medical, Cohort Studies
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