Effects of Payment on User Engagement in Online Courses
Bibliographic record
Abstract
Massive open online courses (MOOCs) have the potential to democratize education by improving access. Although retention and completion rates for nonpaying users have not been promising, these statistics are much brighter for users who pay to receive a certificate upon completing the course. We investigate whether paying for the certificate option can increase engagement with course content. In particular, we consider two effects: (1) the certificate effect, which is the boost in motivation to stay engaged to receive the certificate; and (2) the sunk-cost effect, which arises solely because the user paid for the course. We use data from over 70 courses offered on the Coursera platform and study the engagement of individual participants at different milestones within each course. The panel nature of the data enables us to include controls for intrinsic differences between nonpaying and paying users in terms of their desire to stay engaged. We find evidence that the certificate and sunk-cost effects increase user engagement by approximately 8%–9% and 17%–20%, respectively. Whereas the sunk-cost effect is transient and lasts for only a few weeks after payment, the certificate effect lasts until the participant reaches the grade required to be eligible to receive the certificate. We discuss the implications of our findings for how platforms and content creators may design course milestones and schedule payment of course fees. Given that greater engagement tends to improve learning outcomes, our study serves as an important first step in understanding the role of prices and payment in enabling MOOCs to realize their full potential.
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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 | 0.011 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".