Research and Education Posters Presented at the 121st Virtual Annual Meeting of the American Association of Colleges of Pharmacy, July 13-31, 2020
Bibliographic record
Abstract
Objective: In 2018, we developed and implemented a course report template to serve as a comprehensive tool to gather qualitative and quantitative data to support cyclical program evaluation and inform curriculum renewal.At the individual course instructor level, the course report serves as a reflective teaching tool and generates data on course changes.The primary objective of this study is to examine if the annual course reports identify strengths, challenges, and recommendations for quality improvement.Methods: For the 2018-2019 academic year, we gathered data from the course reports and follow-up meeting notes with course instructors.NVivo was used to conduct thematic analysis of course report qualitative narrative and meeting notes.This was guided by the broad themes of strengths, challenges, and recommendations for quality improvement.This included data on student performance, student course evaluations, approaches to learning, teaching strategies, and assessments.Results: Across all courses, the most common strengths identified were: 1) teaching methodologies (case/problem-based learning and small groups/workshops), and 2) the use of Learning Management System and other related technology.The main challenges identified were logistical issues (eg, scheduling).Finally, three major themes that emerged from recommendations for quality improvement were: 1) increase constructive alignment (eg, learning outcomes, content, etc.), 2) change course format/delivery, and 3) provide additional resources (eg, teaching assistants, clinical instructors, etc.).Conclusions: Implementing the use of an annual course report centralizes important course data into a single document.This allows course coordinators and program leads to engage in meaningful dialogue to identify important emerging themes for consideration during annual course review and renewal.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.410 | 0.090 |
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".