Computer-Based Virtual Environments, Differential Diagnosis, Medical Education, Situated Learning, Preparation for Future Learning, Problem-Solving Before Instruction
A major problem in medical education is that the clinical knowledge and clinical reasoning skills acquired through university teaching do not transfer well to clinical practice. Despite acquiring massive amounts of content knowledge about the functioning of the human body, medical students struggle to transfer that knowledge to one of the core disciplinary practices – differential diagnosis (DD). Related to DD are (a) clinical knowledge which describes declarative/conceptual knowledge about specific diseases and the process of DD and (b) clinical reasoning skills which describe strategic knowledge in the decision-making process associated with DD. The lack to transfer may stem from current methods of instruction which are focused primarily on imparting massive amounts of basic content knowledge without adequate attention to situate this knowledge in disciplinary practice (Collard, Brédart, & Bourguignon, 2016; Norman, 2009). A possible solution to this problem is to expose and link the learning of medical students to the practice of DD. This approach is supported by theories of situated cognition. Whilst we acknowledge that there are several options to integrate situated learning, we aim to explore the use of medical computer-based virtual environment (CVE) simulations. In empirical research it has yet to be shown why and when CVE simulations are effective in medical education and enhancing transfer. Inter alia, the ability to transfer is important because medical students cannot be confronted with all possible situations in their medical studies they will face later in their professional career as doctors. We annotate that in this study we focused clinical knowledge and the conceptual aspects of DD and not on the execution of procedures. Please refer figure 1 or the proposed project approach.
Figure 1. Proposed Project Approach.
With our project we aim to overcome this problem by implementing CVE simulations in medical education courses. Please refer figure 2 for an illustration of the implemented CVE platform. We aimed to enhance isomorphic testing and near and far transfer outcomes of clinical knowledge and clinical reasoning skills through problem-solving in medical CVE simulations in combination with direct instruction. To achieve the proposed research goals, the present project is split into three work packages. Please refer figure 3 for an illustration of the work package flow and the corresponding research goals.
Figure 2. Implemented CVE Platform
Note. Reprinted with permission
In the first work package, we used a problem-solving prior to direct instruction (PS-I) (Kapur & Bielaczyc, 2012; Loibl, Roll, & Rummel, 2017) vs. instruction first (I-PS) experimental design to examine the effect of problem-solving in CVEs on clinical knowledge and clinical reasoning skills isomorphic testing and transfer outcomes and evoked learning mechanisms when combined with direct video instruction in different sequences.
In the second cohort-based study of the present project, we explored the influence of semester-long exposure to problem-solving in CVE preceding direct instruction to interactive group discussions preceding the lecture in an introductory course on performance in a successive advanced course in the medical trajectory, both related to DD. Using a double transfer experimental design, we compared the effects of the two teaching approaches on transfer of clinical knowledge and clinical reasoning skills, and students’ satisfaction and self-confidence in learning.
In the third work package we took up the findings of the first work package and extended the research on the PS-I approach. While using the same experimental design than in work package 1, we altered the timing of provided feedback during the problem-solving phase.
State of the project
Work package 1 took place in the spring semester 2021. We did not find any learning activity sequence to be superior to the other. However, when looking at the two learning activities individually, they found that problem-solving in CVEs as well as direct instruction are equally effective at imparting content knowledge, whereas problem-solving in CVE with formative feedback imparts clinical reasoning skills better than mere instruction.
Work package 2 took place from spring semester 2020 to spring semester 2021. we found both instruction methods to enhance transfer of clinical knowledge, where the business-as-usual method seems to be slightly more effective. Furthermore, we found interactive group discussions to be more effective in enhancing clinical reasoning skills transfer to comprehensive patient scenarios. However, we did not find a difference between groups in transferring clinical reasoning skills to less comprehensive scenarios.
Work package 3 will take place in spring semester 2022.
The findings of the present project have a high level of ecological validity because all studies took place in a realistic setting where (a) where data were derived from business as usual settings or (b) students had to perform learning and testing tasks autonomously from home due to CoVid-19 issues.
Figure 3. Work Package Flow and the Corresponding Research Goals
Abbreviations. CK: clinical knowledge, CRS: clinical reasoning skills.
Collard, A., Brédart, S., & Bourguignon, J.-P. (2016). Context impact of clinical scenario on knowledge transfer and reasoning capacity in a medical problem-based learning curriculum. Higher Education Research & Development, 35(2), 242–253. https://doi.org/10.1080/07294360.2015.1087383
Kapur, M., & Bielaczyc, K. (2012). Designing for Productive Failure. Journal of the Learning Sciences, 21(1), 45–83. https://doi.org/10.1080/10508406.2011.591717
Loibl, K., Roll, I., & Rummel, N. (2017). Towards a Theory of When and How Problem Solving Followed by Instruction Supports Learning. Educational Psychology Review, 29(4), 693–715. https://doi.org/10.1007/s10648-016-9379-x
Norman, G. (2009). Teaching basic science to optimize transfer. Medical Teacher, 31(9), 807–811. https://doi.org/10.1080/01421590903049814
Fässler, C. (2021, August). Computer-Based Virtual Environment Simulations for Differential Diagnosis in Medical Education. Poster presented at the 25th meeting of the JURE Pre-Conference for Research on Learning and Instruction, Online.
Fässler, C. (2021, September). Sequencing of Problem-Solving in CVE Scenarios and Direct Video Instruction. Poster presented at the 17th annual meeting of the GMA (Gemeinschaft für Medizinische Ausbildung), Online.