Enhanced Learning Through Technology in Medical Education

CHER Pei Hua, Jason LEE Wen Yau, and FOO Yang Yann
Duke-NUS Medical School

Pei Hua, Jason, and Yang Yann provide an overview of the activities of their learning community, which was formed to have conversations about and share resources on medical education, technology, and pedagogy.

Cher P. H., Lee, J. W. Y., & Foo Y. Y. (2022, July 28). Enhanced learning through technology in medical education. Teaching Connections. https://blog.nus.edu.sg/teachingconnections/2022/07/27/enhanced-learning-through-technology-in-medical-education/

In this learning community (LC)¹, we gathered a multidisciplinary group of experts to share research on medical education, technology, and pedagogy. The LC was made up of a multidisciplinary team comprising eight faculty members and six professionals from three departments in Duke-NUS Medical School, the NUS Faculty of Dentistry and the Keio-NUS CUTE Center².

During the 13 sharing sessions over the semester, we read articles, shared projects, and introduced new technology tools that can be used for teaching and learning. (See Table 1 in the Appendix for a list of papers discussed and technology tools shared). During each session, we had an average attendance of 9 members and guests (std: 2.29). We discussed research articles covering various teaching and learning topics—AI in education, learning analytics, simulation in healthcare education, evaluation methods in technology-enhanced learning, learning science and game-based learning through project demonstrations—as well as visited labs and learned about new interactive technologies and tools used for teaching and learning. In short, we fostered collaboration across the three departments and most importantly, developed a community of practice.

During our monthly presentations, we experienced how different individuals use tools (e.g. Padlet, Mural, etc) to disseminate teaching content and conduct lessons. We also learned to incorporate a good lesson “hook” to get LC members warmed up and contributing actively during the 1-hour sharing sessions, strategies we can apply to our own classes.

At the end of the 13th sharing session, we conducted a survey to find out our members’ experience of using the different software featured during each sharing session such as their preferences, experience and challenges faced when using the software for teaching. Our findings showed that the top choice of tools used for presentations were MS PowerPoint, Miro, and Zoom Whiteboard. The respondents also indicated that they used these tools to teach in their classes. These tools are mainly class content and lecture delivery tools, communication, collaborative tools, and feedback/polling tools. Respondents also indicated that a combination of in-person, remote synchronous and remote asynchronous tools best fit their teaching needs. In addition, the findings highlighted that respondents mainly need these tools for presentation and distribution of information. MS PowerPoint was highlighted by respondents as being the best tool due to familiarity of use, smooth integration with feedback tools (e.g. PollEverywhere), and being a good presentation tool for in-class illustration and annotation. However, it was also pointed out that MS PowerPoint lacked interactivity for collaborative in-class and remote asynchronous activities. This gap can be bridged by other collaborative tools like Miro and Padlet.

As an LC, we discussed and evaluated the suitability of different pedagogical approaches for different learners and settings. For simulation-based education, for instance, it was crucial to adjust the degree of fidelity (i.e. verisimilitude) based on the trainees’ level of training. While novices may experience training in a quiet environment, scenarios for advanced trainees may include comforting standardised patients who were bleeding profusely and writhing in pain.

At the end of each session, we summarised the discussion for the benefit of other LC members who could not join us for that session. Figure 1 shows a word cloud of our LC’s 13-week sharing sessions, providing insight into the key topics we have shared in our community.

Figure 1. Word cloud of discussions during the LC sharing sessions.

In summary, these sharing sessions were an eye-opener and engendered inspiring exchanges as we learned from one another. We highlighted the importance of not introducing too many tools in our practice, but instead use one new tool repeatedly for the whole semester to improve our skills in using this tool and get students used to it. We also shared the importance of evaluating software tools. Overall, we used the LC as an opportunity to share, learn and explore new teaching tools and pedagogy.

Appendix. List of sharing and special (*) sessions conducted over 13 weeks (in chronological order)

CHER Pei Hua is an Assistant Professor at the Duke-NUS Medical School. Her research interests include AI in healthcare, learning analytics and using applied multi-disciplinary research techniques in machine learning and natural language processing.

Pei Hua can be reached at peihua.cher@duke-nus.edu.sg.

Jason LEE is an Assistant Professor at the Duke-NUS Medical School. His current research and teaching interests involve looking into the use of technology for medical education such as immersive learning, 3D printing, and AR/VR technology.

Jason can be reached at jason.lee@duke-nus.edu.sg.

FOO Yang Yann is an Assistant Professor at Duke-NUS Medical School. Her research interests include facilitating students’ engagement with feedback, and promoting interprofessional collaborative practice.

Yang Yann can be reached at yangyann.foo@duke-nus.edu.sg.

Endnotes

The formation of this learning community (LC) was made possible due to the generous support of a Teaching Enhancement Grant (TEG) from the Centre for Development of Teaching and Learning (CDTL).
The Center’s full name is the KEIO-NUS CUTE (Creating Unique Technology for Everyone) Center.

References

Carpenter, D., Geden, M., Rowe, J., Azevedo, R., & Lester, J. (2020). Automated analysis of middle school students’ written reflections during game-based learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12163 LNAI, 67–78. https://doi.org/10.1007/978-3-030-52237-7_6/TABLES/3

Cook, D. A., & Ellaway, R. H. (2015). Evaluating technology-enhanced learning: A comprehensive framework. Medical Teacher , 37(10), 961–970. https://doi.org/10.3109/0142159X.2015.1009024

Jeno, L. M., Vandvik, V., Eliassen, S., & Grytnes, J. A. (2019). Testing the novelty effect of an m-learning tool on internalization and achievement: A Self-determination Theory approach. Computers & Education, 128, 398–413. https://doi.org/10.1016/J.COMPEDU.2018.10.008

Larusson, J. A., & White, B. (2014). Learning analytics: From research to practice. In Learning Analytics: From Research to Practice. Springer New York. https://doi.org/10.1007/978-1-4614-3305-7

Marton, F. (2009). Sameness and difference in transfer. Journal of the Learning Sciences, 15(4), 499–535. https://doi.org/10.1207/S15327809JLS1504_3

Mills, B. W., Carter, O. B. J., Rudd, C. J., Claxton, L. A., Ross, N. P., & Strobel, N. A. (2016). Effects of low-versus high-fidelity simulations on the cognitive burden and performance of entry-level paramedicine students: A mixed-methods comparison trial using eye-tracking, continuous heart rate, difficulty rating scales, video observation and interviews. Simulation in Healthcare, 11(1), 10–18. https://doi.org/10.1097/SIH.0000000000000119

Motola, I., Devine, L. A., Chung, H. S., Sullivan, J. E., & Issenberg, S. B. (2013). Simulation in healthcare education: a best evidence practical guide. AMEE Guide No. 82. Medical Teacher, 35(10). https://doi.org/10.3109/0142159X.2013.818632

Rooney, P. (2012). A theoretical framework for serious game design: Exploring pedagogy, play and fidelity and their implications for the design process. International Journal of Game-Based Learning (IJGBL), 2(4), 41-60. http://doi.org/10.4018/ijgbl.2012100103

Sukhera, J., & Poleksic, J. (2021). Adapting compassion education through technology-enhanced learning: An exploratory study. Academic Medicine, 1013–1020. https://doi.org/10.1097/ACM.0000000000003915

Ullmann, T. D. (2019). Automated analysis of reflection in writing: Validating machine learning approaches. International Journal of Artificial Intelligence in Education, 29(2), 217–257. https://doi.org/10.1007/s40593-019-00174-2