Development of an electronic learning progression dashboard to monitor student clinical experiences
Bibliographic record
Abstract
INTRODUCTION: Clinical experience tracking mechanisms for students at dental schools provide patient assignment, student experience, and learning progression feedback. The purpose of this study was to evaluate dental students' clinical experiences following the implementation of a learning progression dashboard (LPD). METHODS: After developing and deploying an electronic LPD using PHP, secondary data analysis on dental students' clinical experiences from 2017-2019 was conducted. Student experience differences were compared between the year before continuous use of the LPD and the first year using it. LPD data contained the required clinical procedures dentistry students must perform across all disciplines and the number of planned, in progress, and completed tasks each student has accomplished. Using two time points, the students' experiences were compared. Univariate statistics and independent t-tests were conducted in R for detecting the differences in the number and categories of codes. RESULTS: The number and category of codes showed significant differences between the academic year 2017-2018 and 2018-2019 for both third- and fourth-year dental students after one and two terms. Overall, students recorded a 26% greater number of treatment codes and experienced a 26% greater number of code categories compared to the previous year. CONCLUSION: Applying information management methods such as dashboards can better inform educators on student clinical experiences and improve clinical learning outcomes for students.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".