Background COVID-19 caused significant confusion around the world, and dental education was no exception. Therefore, in line with the demands of the times, this study sought to determine the applicability of online active learning to dental education. Methods This study was conducted in the second semester of 2020 at a school of dentistry in a selective university in Korea. A total of 114 dental students were recruited. Participants were assigned to four different groups (lecture and discussion [LD], lecture and discussion with instructor’s worksheet [LW], self-study and discussion [SSD], and self-study and discussion with instructor’s worksheet [SW]) using the random breakout room function in the Zoom video conference application. Their final test scores were then analyzed using analysis of variance and the online active learning results were compared with the offline learning results. Results The scores were highest for the transfer type items in the SSD group, followed by the SW group and the two lecture groups, which had no significant differences. These scores and pattern differences between the groups were similar for all items. The results suggested that studying by oneself rather than simply listening to lectures enhanced the effects of the discussions and led to higher learning outcomes. In addition, the effect of the instructor's intervention in the middle of the discussion varied depending on the pre-learning activities of discussion. As with previous offline experiments, self-study followed by group discussion had higher learning outcomes for both the verbatim and transfer type items. Conclusions In agreement with the Interactive, Constructive, Active, and Passive (ICAP) framework and other active learning theories, the findings clearly indicated that online active learning was applicable to dental students, and when self-study precedes discussion, the learning is richer and the learning outcomes are better.

Background: Cerebral amyloid beta (Aβ) is a hallmark of Alzheimer’s disease (AD). Aβ can be detected in vivo with amyloid imaging or cerebrospinal fluid assessments. However, these technologies can be both expensive and invasive, and their accessibility is limited in many clinical settings. Hence the current study aims to identify multivariate cost-efficient markers for Aβ positivity among non-demented individuals using machine learning (ML) approaches. Methods: The relationship between cost-efficient candidate markers and Aβ status was examined by analyzing 762 participants from the Alzheimer’s Disease Neuroimaging Initiative-2 cohort at baseline visit (286 cognitively normal, 332 with mild cognitive impairment, and 144 with AD; mean age 73.2 years, range 55–90). Demographic variables (age, gender, education, and APOE status) and neuropsychological test scores were used as predictors in an ML algorithm. Cerebral Aβ burden and Aβ positivity were measured using 18 F-florbetapir positron emission tomography images. The adaptive least absolute shrinkage and selection operator (LASSO) ML algorithm was implemented to identify cognitive performance and demographic variables and distinguish individuals from the population at high risk for cerebral Aβ burden. For generalizability, results were further checked by randomly dividing the data into training sets and test sets and checking predictive performances by 10-fold cross-validation. Results: Out of neuropsychological predictors, visuospatial ability and episodic memory test results were consistently significant predictors for Aβ positivity across subgroups with demographic variables and other cognitive measures considered. The adaptive LASSO model using out-of-sample classification could distinguish abnormal levels of Aβ. The area under the curve of the receiver operating characteristic curve was 0.754 in the mild change group, 0.803 in the moderate change group, and 0.864 in the severe change group, respectively. Conclusion: Our results showed that the cost-efficient neuropsychological model with demographics could predict Aβ positivity, suggesting a potential surrogate method for detecting Aβ deposition non-invasively with clinical utility. More specifically, it could be a very brief screening tool in various settings to recruit participants with potential biomarker evidence of AD brain pathology. These identified individuals would be valuable participants in secondary prevention trials aimed at detecting an anti-amyloid drug effect in the non-demented population.

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Background The ICAP framework based on Cognitive Science posits four modes of cognitive engagement: Interactive, Constructive, Active, and Passive. Focusing on the wider applicability of discussion as interactive engagement in medical education, we investigated the effect of discussion when self-study preceded it and further investigated the effect of generating questions before discussions. Methods This study was conducted in the second semester of 2018, and 129 students majoring in health professions, including medicine, dentistry, veterinary medicine, and nursing, participated. The students were assigned into four different trial groups, who were asked to fill out a Subjective Mental Effort Questionnaire after completing each session. Their performance in post-test scores and their mental efforts were analyzed. Results A Bonferroni test for group comparison indicated that the self-study and question-generated group had the highest performance and that the lecture and question-received group had the lowest performance when comparing the total score. By using a mediation model, it was confirmed that the participants who showed a higher level of testing mental effort also showed higher levels of studying and discussion mental effort. Conclusions Our findings support the ICAP framework and provide practical implications for medical education, representing the fact that students learn more when they are involved in active learning activities, such as self-study and question generation, prior to discussions.