Is the Understanding Dementia Massive Open Online Course Accessible and Effective for Everyone? Native Versus Non-Native English Speakers
Bibliographic record
Abstract
Most massive open online courses (MOOCs) are offered in English, including those offered by non-English speaking universities. The study investigated an identified English language dementia MOOC’s accessibility and effectiveness in improving the dementia knowledge of non-native English speaker participants. A total of 6,389 enrolees (age range 18–82 years; 88.4% female) from 67 countries was included in analyses. Dementia knowledge was measured by the Dementia Knowledge Assessment Scale (DKAS) before and after the MOOC completion. Rates of completion were also compared. Native English speakers (n = 5,320) were older, more likely to be female, less likely to be employed, and had lower educational attainment than non-native English speakers (n = 1025). Native English speakers were also more likely to care for or have cared for a family member or friend living with dementia than were non-native English speakers. Native English speakers had a significantly higher DKAS score both pre- (M = 33.0, SD = 9.3) and post-MOOC (M = 44.2, SD = 5.5) than did non-native English speakers (M = 31.7, SD = 9.1; and M = 40.7, SD = 7.7 for pre- and post-MOOC, respectively). Non-native English speakers with low pre-MOOC dementia knowledge scores gained significantly less dementia knowledge following course completion than did native English speakers (p <.001, adjusted for age and education). There was no significant difference between the two groups in their likelihood of completing the MOOC. Our findings suggest that non-native English speakers are motivated and able to complete the MOOC at similar rates to native English speakers, but the MOOC is a more effective educational intervention for native English speakers with low dementia knowledge.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".