Online learning in Health Professions Education. Part 1: Teaching and learning in online environments: AMEE Guide No. 161
Bibliographic record
Abstract
Online learning in Health Professions Education (HPE) has been evolving over decades, but COVID-19 changed its use abruptly. Technology allowed necessary HPE during COVID-19, but also demonstrated that many HP educators and learners had little knowledge and experience of these complex sociotechnical environments. Due to the educational benefits and flexibility that technology can afford, many higher education experts agree that online learning will continue and evolve long after COVID-19. As HP educators stand at the crossroads of technology integration, it is important that we examine the evidence, theories, advantages/disadvantages, and pedagogically informed design of online learning. This Guide will provide foundational concepts and practical strategies to support HPE educators and institutions toward advancing pedagogically informed use of online HPE. This Guide consists of two parts. The first part will provide an overview of evidence, theories, formats, and educational design in online learning, including contemporary issues and considerations such as learner engagement, faculty development, inclusivity, accessibility, copyright, and privacy. The second part (to be published as a separate Guide) focuses on specific technology tool types with practical examples for implementation and integration of the concepts discussed in Guide 1, and will include digital scholarship, learning analytics, and emerging technologies. In sum, both guides should be read together, as Guide 1 provides the foundation required for the practical application of technology showcased in Guide 2.Please refer to the video abstract for Part 1 of this Guide at https://bit.ly/AMEEGuideOnlineLearning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.074 |
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".