Method: A quasi-experimental study was conducted using 254 first-year medical students with no prior exposure to the lecture topic during the 2016/17 and 2017/18 academic sessions. The students from each batch were divided into two groups and exposed to different video material. Group A watched an action movie, while Group B watched an educational video related to the lecture topic. After 15 min, both groups attended a lecture on the gross anatomy of the heart, which was delivered by a qualified anatomist. At the end of the lecture, their understanding of the material was measured through a post-lecture test using ten vetted multiple choice true/false questions.
Results: Group B's test scores were found to be significantly higher than Group A's (p > 0.001, t-stats [df] = -4.21 [252]).
Conclusion: This study concluded that the pre-lecture activity had successfully provided the students with some prior knowledge of the subject before they attended the lecture sessions. This finding was aligned with cognitive load theory, which describes a reduction in learners' cognitive load when prior knowledge is stimulated.
METHODS: This was a qualitative phenomenology study conducted on 116 second-year medical students from two Malaysian public universities via teleconferencing applications that allowed synchronous small-group activities. Each group was given a different link to 10 GJ slides that featured plain anatomy diagrams and instructions for the group task. Upon completion of the tasks, the students presented their tasks to the whole class. An online feedback form was distributed at the end of the practical session to explore the experience of the students when using the tool.
RESULTS: Thematic analysis of student responses generated seven themes that reflected perceived learning benefits, challenges faced by the students, and suggestions for future improvement.
CONCLUSIONS: These findings suggest that GJ is a useful tool for promoting collaborative learning in virtual anatomy education. Nevertheless, the impact of this tool on the attainment of learning outcomes remains unknown. Hence, more widescale research is needed to confirm our findings.
METHODS: 18 voluntarily participants were recruited from the Canterbury and Otago region of New Zealand to take part in a Dynamic Insulin Sensitivity and Secretion Test (DISST) clinical trial. A total of 46 DISST data were collected. However, due to ambiguous and inconsistency, 4 data had to be removed. Analysis was done using MATLAB 2020a.
RESULTS AND DISCUSSION: Results show that, with 42 gathered dataset, the ANN generates higher gains, ∅P = 20.73 [12.21, 28.57] mU·L·mmol-1·min-1 and ∅D = 60.42 [26.85, 131.38] mU·L·mmol-1 as compared to the linear least square method, ∅P = 19.67 [11.81, 28.02] mU·L·mmol-1 ·min-1 and ∅D = 46.21 [7.25, 116.71] mU·L·mmol-1. The average value of the insulin sensitivity (SI) of ANN is lower with, SI = 16 × 10-4 L·mU-1 ·min-1 than the linear least square, SI = 17 × 10-4 L·mU-1 ·min-1.
CONCLUSION: Although the ANN analysis provided a lower SI value, the results were more dependable than the linear least square model because the ANN approach yielded a better model fitting accuracy than the linear least square method with a lower residual error of less than 5%. With the implementation of this ANN architecture, it shows that ANN able to produce minimal error during optimization process particularly when dealing with outlying data. The findings may provide extra information to clinicians, allowing them to gain a better knowledge of the heterogenous aetiology of diabetes and therapeutic intervention options.