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Exposure to real-life clinical cases has been regarded as the optimal method of achieving deep learning in medical education. Case-based e-learning (CBEL) has been considered a promising alterative to address challenges in the availability of teaching cases and standardizing case exposure. While the use of CBEL has been positive in veterinary medical education, insight into students' learning experience with a CBEL tool have not been considered. This study investigates students' views around the utility and usability of a CBEL tool, as well as perceived effectiveness, clinical confidence, and impact of veterinary students' learning preferences on CBEL use. Through focus groups as well as pre- and post-use questionnaires, students expressed that the design and utility of the online cases, including their authenticity, played an instrumental role in perspectives and acceptance of the CBEL tool. Students perceived the CBEL tool as highly effective in both achieving CBEL outcomes and teaching a methodical approach to a clinical case. CBEL elements were also perceived to potentially contribute to increased clinical confidence after CBEL use. Additionally, exploration of students' preferred approach to learning revealed that hands-on learners and those who prefer to learn by practicing and applying knowledge were more likely to show positive perceptions of a CBEL tool. Findings of this study can help guide educators in the future design and implementation of online cases in various capacities and provide a platform for further exploration of the effectiveness and use of CBEL in veterinary medical education.
This article was published in the following journal.
Name: Journal of veterinary medical education
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Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
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