Coursera’s introductory human physiology course: Factors that characterize successful completion of a MOOC
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".