The Massive Online Needs Assessment (MONA) to inform the development of an emergency haematology educational blog series
Bibliographic record
Abstract
BACKGROUND: Online educational resources are criticized as being teacher-centred, failing to address learner's needs. Needs assessments are an important precursor to inform curriculum development, but these are often overlooked or skipped by developers of online educational resources due to cumbersome measurement tools. Novel methods are required to identify perceived and unperceived learning needs to allow targeted development of learner-centred curricula. OBJECTIVES: To evaluate the feasibility of performing a novel technique dubbed the Massive Online Needs Assessment (MONA) for the purpose of emergency haematology online educational curricular planning, within an online learning community (affiliated with the Free Open Access Medical education movement). METHODS: An online survey was launched on CanadiEM.org using an embedded Google Forms survey. Participants were recruited using the study website and a social media campaign (utilizing Twitter, Facebook, Blogs, and a poster) targeting a specific online community. Web analytics were used to monitor participation rates in addition to survey responses. RESULTS: The survey was open from 20 September to 10 December 2016 and received 198 complete responses representing 6 medical specialties from 21 countries. Most survey respondents identified themselves as staff physicians (n = 109) and medical trainees (n = 75). We identified 17 high-priority perceived needs, 17 prompted needs, and 10 topics with unperceived needs through our MONA process. CONCLUSIONS: A MONA is a feasible, novel method for collecting data on perceived, prompted, and unperceived learning needs to inform an online emergency haematology educational blog. This methodology could be useful to the developers of other online education resources.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".