Academic Primer Series: Key Papers About Teaching with Technology
Bibliographic record
Abstract
INTRODUCTION: To appeal to this current generation of learners, educators must embrace the use of technology. However, educators must balance newer, novel technologies with traditional methods to achieve the best learning outcomes. Therefore, we aimed to review several papers useful for faculty members wishing to incorporate technology into instructional design. METHODS: We identified a broad list of papers relevant to teaching and learning with technology within the online discussions of the Academic Life in Emergency Medicine (ALiEM) Faculty Incubator. This list was augmented with suggestions by a guest expert (BT) and an open call on Twitter (tagged with the #meded and #FOAMed hashtags) yielding 24 papers. We then conducted a modified three-round Delphi process within the authorship group, including junior and senior faculty members, to identify the most impactful papers. RESULTS: We pared the list of 24 papers to five that were most highly rated. Two were research papers and three were commentaries or editorials. The authorship group reviewed and summarized these papers with specific consideration to their value to junior educators and faculty developers. CONCLUSION: This is a key reading list for junior faculty members and faculty developers interested in teaching with technology. The commentary contextualizes the importance of these papers for medical educators, to optimize use of technology in their teaching or incorporate into faculty development.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".