Unintended consequences of technology in competency-based education: A qualitative study of lessons learned in an OtoHNS program
Bibliographic record
Abstract
BACKGROUND: Formative feedback and entrustment ratings on assessments of entrustable professional activities (EPAs) are intended to support learner self-regulation and inform entrustment decisions in competency-based medical education. Technology platforms have been developed to facilitate these goals, but little is known about their effects on these new assessment practices. This study investigates how users interacted with an e-portfolio in an OtoHNS surgery program transitioning to a Canadian approach to competency-based assessment, Competence by Design. METHODS: We employed a sociomaterial perspective on technology and grounded theory methods of iterative data collection and analysis to study this OtoHNS program's use of an e-portfolio for assessment purposes. All residents (n = 14) and competency committee members (n = 7) participated in the study; data included feedback in resident portfolios, observation of use of the e-portfolio in a competency committee meeting, and a focus group with residents to explore how they used the e-portfolio and visualize interfaces that would better meet their needs. RESULTS: Use of the e-portfolio to document, access, and interpret assessment data was problematic for both residents and faculty, but the residents faced more challenges. While faculty were slowed in making entrustment decisions, formative assessments were not actionable for residents. Workarounds to these barriers resulted in a "numbers game" residents played to acquire EPAs. Themes prioritized needs for searchable, contextual, visual, and mobile aspects of technology design to support use of assessment data for resident learning. CONCLUSION: Best practices of technology design begin by understanding user needs. Insights from this study support recommendations for improved technology design centred on learner needs to provide OtoHNS residents a more formative experience of competency-based training.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".