Exploring Demographics and Students’ Motivation as Predictors of Completion of a Massive Open Online Course
Bibliographic record
Abstract
This paper investigates the degree to which different variables affect the completion of a Massive Open Online Course (MOOC). Data on those variables, such as age, gender, English proficiency, education level, and motivation for course enrollment were first collected through a pre-course survey. Next, course completion records were collected via the Coursera database. Finally, multiple binomial logistic regression models were used to identify factors related to MOOC completion. Although students were grouped according to their preferences, working in groups did not affect students’ likelihood for MOOC completion. Also, other variables such as age, the institution hosting the MOOC, academic program alignment with students’ needs, and students’ intention to complete the course all affected their probability of MOOC completion. This study contributes to the literature by indicating the factors that influence the probability of MOOC completion. Results show that older participants (age > 50 years old) have higher probability of completing the MOOC. Students’ MOOC completion also increases when the MOOC provides experiences that add to students’ current academic backgrounds and when they are hosted by institutions with a strong academic reputation. Based on these factors, this study contributes to research methods in MOOCs by proposing a model that is aligned with the most important factors predicting completion as recommended by the current MOOC literature. For the next phase of assigning learners to work in groups, findings from this study also suggest that MOOC instructors should provide assistance for group work and monitor students’ collaborative processes.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".