Communication, learning and assessment: Exploring the dimensions of the digital learning environment
Bibliographic record
Abstract
Advances in technology make it possible to supplement in-person teaching activities with digital learning, use electronic records in patient care, and communicate through social media. This relatively new "digital learning environment" has changed how medical trainees learn, participate in patient care, are assessed, and provide feedback. Communication has changed with the use of digital health records, the evolution of interdisciplinary and interprofessional communication, and the emergence of social media. Learning has evolved with the proliferation of online tools such as apps, blogs, podcasts, and wikis, and the formation of virtual communities. Assessment of learners has progressed due to the increasing amounts of data being collected and analyzed. Digital technologies have also enhanced learning in resource-poor environments by making resources and expertise more accessible. While digital technology offers benefits to learners, the teachers, and health care systems, there are concerns regarding the ownership, privacy, safety, and management of patient and learner data. We highlight selected themes in the domains of digital communication, digital learning resources, and digital assessment and close by providing practical recommendations for the integration of digital technology into education, with the aim of maximizing its benefits while reducing risks.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".