METHODS: Curriculum mapping was conducted for the Year 2 undergraduate dental materials science course (Bachelor of Dental Surgery programme) in a Malaysian dental school. Based on Harden's framework, the following steps were used to map the curriculum of the institutional dental materials science course: (1) scoping the task; (2) deciding the mapping format; (3) populating the windows, and (4) establishing the links. Two analysts reviewed the curriculum independently. Their respective analyses were compared, and discrepancies were discussed until reaching a consensus. A SWOT analysis was also conducted to evaluate the strengths, weaknesses, opportunities, and threats associated with the curriculum.
RESULTS: Course learning outcomes, course contents, levels of cognitive and psychomotor competencies, learning opportunities, learning resources, learning locations, assessments, timetable, staff, curriculum management and students' information were successfully scoped from the institutional dental materials science course. The present curriculum's strengths included comprehensiveness, alignment with standards, adequate learning opportunities, well-defined assessment methods, and sufficient learning resources. However, the identified weaknesses were repetition in curriculum content, limited emphasis on the psychomotor domain, dependency on a single academic staff, and limited integration of technology. The SWOT analysis highlighted the opportunities for curriculum improvement, such as revising repetitive content, emphasising the psychomotor domain, and incorporating advanced teaching strategies and technology.
CONCLUSIONS: The present dental materials science curriculum demonstrated several strengths with some areas for improvement. The findings suggested the need to revise and optimise the course content to address gaps and enhance student learning outcomes. Ongoing monitoring and evaluation are necessary to ensure the curriculum remains aligned with emerging trends and advancements in dental materials science.
METHOD: An explanatory mixed method research design was carried out on first year medical students at a private university in Malaysia. In Phase I, a survey was conducted to explore the effectiveness of jigsaw learning. Descriptive and inferential statistics were calculated using SPSS. In Phase II, a focus group interview was conducted to explore their in-depth experiences. Qualitative data were thematically analysed.
RESULTS: Fifty-seven students participated in the survey and seven students took part in the focus group interview. Quantitative data analysis showed a statistically significant improvement in the student's individual accountability, promotive interaction, positive interdependence, interpersonal skill, communication skill, teamwork skill, critical thinking and consensus building after jigsaw learning sessions. Qualitative data explained their experiences in-depth.
CONCLUSION: Jigsaw cooperative learning improves collaboration, communication, cooperation and critical thinking among the undergraduate medical students. Educators should use jigsaw learning methods to encourage effective collaboration and team working. Future studies should explore the effectiveness of the jigsaw cooperative learning technique in promoting interprofessional collaboration in the workplace.
METHODS: A newly developed six-week VC module was implemented online through asynchronous microlearning and synchronous simulation-based experiential learning modalities. Clinical students in years 4 and 5 and fresh graduates, who had not started pre-registration house officer or residency programmes, were invited to participate. Training outcomes using checklist-based video-recorded assessments of VC encounters between medical students and simulated patients were compared. Each video was independently assessed by two facilitators trained in VC teaching and assessment, using a direct observed virtual consultation skills checklist derived from established VC competencies. The participants completed course evaluations electronically as additional outcome measures.
RESULTS: Fifty-two clinical phase medical students and alumni completed both the instructional and practical phases of this module. Altogether, 45 (95.7%) students found the module beneficial, and 46 (95.9%) reported increased self-efficacy for conducting VC. In total, 46 (95.9%) students would recommend the course to others. Post-test results showed a significant increase in the students' abilities to conduct a VC (t-test = 16.33, p
METHODS: Ten students who previously underwent the learning module were recruited through purposive sampling. The inclusion criteria were: (a) Fourth-year medical students; and (b) Completed psychiatry posting with the new module. Students who dropped out or were unable to participate in data collection were excluded. Two online focus group discussions (FGDs) with five participants each were conducted by an independent facilitator, guided by a questioning route. The data were transcribed verbatim and coded using the thematic analysis approach to identify themes.
RESULTS: Three main themes of their learning experience were identified: (1) fulfilment of the desired pedagogy (2), realism of the clinical case, and (3) ease of use related to technical settings. The pedagogy theme was further divided into the following subthemes: level of entry for students, flexibility of presentation of content, provision of learning guidance, collaboration with peers, provision of feedback, and assessment of performance. The realism theme had two subthemes: how much the virtual patient experience mimicked an actual patient and how much the case scenario reflected real conditions in the Malaysian context. The technical setting theme entailed two subthemes: access to the software and appearance of the user interface. The study findings are considered in the light of learning formats, pedagogical and learning theories, and technological frameworks.
CONCLUSIONS: The findings shed light on both positive and negative aspects of using virtual patients for medical students' psychiatry posting, which opens room for further improvement of their usage in undergraduate psychiatry education.
METHODS: Final-year medical students across three campuses (Ireland, Bahrain and Malaysia) were invited to share experiences of feedback in individual semi-structured interviews. The data were thematically analysed and explored through the lens of self-regulatory learning theory (SRL).
RESULTS: Feedback interacts with learners' knowledge and beliefs about themselves and about learning. They use feedback to change both their cognitive and behavioural learning strategies, but how they choose which feedback to implement is complex. They struggle to generate learning strategies and expect teachers to make sense of the "how" in addition to the "what"" in planning future learning. Even when not actioned, learners spend time with feedback and it influences future learning.
CONCLUSION: By exploring our findings through the lens of self-regulation learning, we advance conceptual understanding of feedback responses. Learners' ability to generate "next steps" may be overestimated. When feedback causes negative emotions, energy is diverted from learning to processing distress. Perceived non-implementation of feedback should not be confused with ignoring it; feedback that is not actioned often impacts learning.
METHODS: A total of 255 dental students in Universiti Malaya completed the modified Index of Learning Styles (m-ILS) questionnaire containing 44 items which classified them into their respective LS. The collected data, referred to as dataset, was used in a decision tree supervised learning to automate the mapping of students' learning styles with the most suitable IS. The accuracy of the ML-empowered IS recommender tool was then evaluated.
RESULTS: The application of a decision tree model in the automation process of the mapping between LS (input) and IS (target output) was able to instantly generate the list of suitable instructional strategies for each dental student. The IS recommender tool demonstrated perfect precision and recall for overall model accuracy, suggesting a good sensitivity and specificity in mapping LS with IS.
CONCLUSION: The decision tree ML empowered IS recommender tool was proven to be accurate at matching dental students' learning styles with the relevant instructional strategies. This tool provides a workable path to planning student-centered lessons or modules that potentially will enhance the learning experience of the students.
OBJECTIVE: This research aims to determine factors influencing students' behavioural intention to use Rain Classroom.
METHODS: In this cross-sectional and correlational investigation, 1138 medical students from five medical universities in Guangxi Province, China, made up the sample. This study added self-efficacy (SE), motivation (MO), stress (ST), and anxiety (AN) to the UTAUT framework. This study modified the framework by excluding actual usage variables and focusing only on intention determinants. SPSS-26 and AMOS-26 were used to analyze the data. The structural equation modelling technique was chosen to confirm the hypotheses.
RESULTS: Except for facilitating conditions (FC), all proposed factors, including performance expectancy (PE), effort expectancy (EE), social influence (SI), self-efficacy (SE), motivation (MO), anxiety (AN), and stress (ST), had a significant effect on students' behavioural intentions to use Rain Classroom.
CONCLUSIONS: The research revealed that the proposed model, which was based on the UTAUT, is excellent at identifying the variables that influence students' behavioural intentions in the Rain Classroom. Higher education institutions can plan and implement productive classrooms.
METHOD: This study was designed based on PRISMA guidelines. PubMed, Scopus, and Google Scholar databases were searched with relevant keywords. After study selection according to inclusion criteria, data of knowledge and attitude were extracted for meta-analysis.
RESULT: Twenty-two studies included 8491 participants were included in this meta-analysis. The pooled analysis revealed a proportion of 0.44 (95%CI = [0.34, 0.54], P