Coursera’s introductory human physiology course: Factors that characterize successful completion of a MOOC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Since Massive Open Online Courses (MOOCs) are accessible by anyone in the world at no cost, they have large enrollments that are conducive to educational research. This study examines students in the Coursera MOOC, Introductory Human Physiology. Of the 33,378 students who accessed the course, around 15,000 students responded to items on the pre-course survey about their age, educational background, proficiency in English, and plans for participating in the course. We categorized students who completed the pre-course survey into groups based on the number of exams completed and corresponding course achievement level. We used Chi-square goodness of fit tests to analyze the distribution of students’ responses on the pre-course survey and associated achievement level. Of the students who responded to the pre-course survey and passed with distinction, a larger percentage self-identified as fluent in English while a smaller percentage self-identified as beginners. Students with graduate degrees were more likely to pass the course or pass with distinction than students with only some college experience or a bachelor’s degree. Students who completed either some or all exams were more likely to self-report intention to complete all course activities than students who did not take any exams. A greater proportion of students who passed the course or passed with distinction posted two or more times on the course discussion forum. Understanding MOOC students and the characteristics that lead to their success will enable modification to courses for increased student achievement and may also inform teaching in the traditional classroom.</p>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it