Grit and Intention: Why Do Learners Complete MOOCs?
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
In recent years there has been considerable interest in how many learners complete MOOCs, and what factors during usage can predict completion. Others, however, have argued that many learners never intend to complete MOOCs, and take MOOCs for other reasons. There has been qualitative research into why learners take MOOCs, but the link between learner goals and completion has not been fully established. In this paper, we study the relationship between learner intention to complete a MOOC and their actual completion status. We compare that relationship to the degree to which MOOC completion is predicted by other domain-general motivational factors such as grit, goal orientation, academic efficacy, and the need for cognition. We find that grit and goal orientation are associated with course completion, with grit predicting course completion independently from intention to complete, and with comparable strength.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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