Thriving in foreign learning environments: The case of hands‐on activities in early years of dental education
Bibliographic record
Abstract
OBJECTIVES: Hands-on learning environments can challenge learners' wellbeing in dental education, given their unfamiliarity with students. As today's learners are more aware about their wellbeing needs, it is important to explore the depth and complexity of the challenges they experience and provide them with the necessary support strategies. This study aims to identify the challenges and sources of cognitive overload of early years' dental students across two time-points: 2012 and 2022. We also aim to describe the students' recommendations for future program revision considerations to support students' wellbeing. METHODS: This study employs both qualitative and quantitative methods. Qualitatively, we utilized an Interpretive Description approach and conducted focus groups with first-year dental students in 2023. Quantitively, we utilized first-year dental students' responses to the Study Habits survey administered in 2012/2013. RESULTS: Five main concerns and sources of cognitive load emerged from the focus groups and survey data: steepness of the learning curve, inconsistent feedback, stigma around asking for support, structural and organizational challenges, and lack of resources. Students also identified several suggestions to support their wellbeing, including time, instructor support, non-graded exercises, additional resources, and re-organizing the curriculum. CONCLUSION: This study adopts a wellbeing lens to examine students' transition into hands-on learning activities. These findings were utilized to propose the TIPSS Support Framework (Time, Instructor Capacity Building, Peer Learning and Other Resources, Safe Learning Spaces, and Spiraling Curriculum). The proposed model can serve as a prototype for future studies to explore its applicability and effectiveness in other dental programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".