Insights from a massive open online course (MOOC) for medical education (2014-2018)
Bibliographic record
Abstract
Background Massive open online courses (MOOCs) are technological innovations have been successfully applied in a wide variety of disciplines to deliver quality online education. These courses are an area of intense focus of educational research. Preliminary studies have shown MOOCs to be effective means of delivering medical education. This study reports data on course completion rates and the geographic reach of a MOOC designed for medical education. Methods A online course designed as for a 4th year medical school elective was opened as a free to take MOOC in August, 2014. The course is offered in English with subtitles via Udemy.com. Data regarding completion rates were obtained from the course management interface of the MOOC, data regarding the geographic reach of the course was obtained from Google Analytics. All data is anonymous, aggregated, and studied retrospectively. The intended course audience was fourth year medical students in the United States, but enrollment was open to all. Results MOOC enrollment reached 5,586 students by February, 2018. Completion rates were low (5%), with 8% completing 50% or more of the MOOC. 80% of students did not complete a single course element. Students enrolled from 161 different countries based on localization by Google Analytics. The most common countries students enrolled from were the United States (46%), India (6%), the United Kingdom (4%), Egypt (2.5%), Canada (2.5%), Australia (2%), China (2%), Germany (1.5%), Brazil (1.5%), and Saudi Arabia (1.5%). Conclusions Course enrollment included 5,586 students from 161 different countries. Course completion rates were low, but consistent with other scientific MOOCs designed for high level audiences that are open for public enrollment. These results also show the potential global reach of a MOOC. These factors of high enrollment, low course completion, but global reach are unique challenges for medical educators who deliver content via MOOC technology. Further study is needed to further define the role of MOOCs in medical education.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".