Pedagogical Approaches and Learning Activities, Content, and Resources Used in the Design of Massive Open Online Courses (MOOCs) in the Health Sciences: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Developing online, widely accessible educational courses, such as Massive Open Online Courses (MOOCs), offer novel opportunities to advancing academic research and the educational system in resource-constrained countries. Despite much literature on the use of design-related features and principles of different pedagogical approaches when developing MOOCs, there are reports of inconsistency between the pedagogical approach and the learning activities, content, or resources in MOOCs. OBJECTIVE: We present a protocol for a scoping review aiming to systematically identify and synthesize literature on the pedagogical approaches used, and the learning activities, content, and resources used to facilitate social interaction and collaboration among postgraduate learners in MOOCs across the health sciences. METHODS: We will follow a 6-step procedure for scoping reviews to conduct a search of published and gray literature in the following databases: Medline via Ovid, ERIC, SCOPUS, Web of Science, and PsychINFO. Two reviewers will screen titles, abstracts, and relevant full texts independently to determine eligibility for inclusion. The team will extract data using a predefined charting form and synthesize results in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. RESULTS: The scoping review is currently ongoing. As of March 2022, we have performed initial data searches and screened titles and abstracts of the studies we found but revised the search string owing to inaccurate results. We aim to start analyzing the data in June 2022 and expect to complete the scoping review by February 2023. CONCLUSIONS: With the results of this review, we hope to report on the use of pedagogical approaches and what learning activities, content, and resources foster social and collaborative learning processes, and to further elucidate how practitioners and academics can harvest our findings to bridge the gap between pedagogics and learning activities in the instructional design of MOOCs for postgraduate students in the health sciences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35878.
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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 | 0.156 | 0.120 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 0.016 |
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".