When quick response codes didn’t do the trick
Bibliographic record
Abstract
Medical education programs in the United States or Canada comply with the Liaison Committee on medical education standards to ensure their graduates provide proficient medical care. One standard includes student development as a lifelong learner. The competency of lifelong learning is developed through self-directed activities such as students evaluating their learning objectives and resources without external help.Quick response (QR) codes were the technological tools introduced in a traditional medical institution to enhance students' self-directed initiative to tap resources. Relevant lecture objectives and other information such as supplemental discipline content, reading assignments and web-based link resources were embedded into codes and 'pasted' onto all pages of their course PDF handouts. It was anticipated that most students had access to smart phones to conveniently scan the codes and retrieve the information.However, an in-class survey conducted showed that only 30% of the students found the QR codes useful. Further questioning revealed that some students just didn't know how to use the codes or didn't think the information embedded was worth the effort to decrypt. Although students were tech-savvy in the social and entertainment realms, they were not adept in the use of technology for educational purposes.QR codes presented several theoretical, pedagogical advantages to enhance experiential and self-directed learning. However, implementation among students, in a traditional classroom, required prior instructions on usage. Student feedback was also imperative when introducing novel, innovative tools like QR codes.
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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 | 0.021 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.078 | 0.052 |
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".