Knowledge Marketplaces: An Analysis of the Influence of Business Models on Instructors’ Motivations and Strategies
Bibliographic record
Abstract
Unlike MOOC platforms such as Coursera or edX, which typically partner with institutions of higher education, online knowledge marketplaces allow anyone to broadcast courses and charge for them. In this article, we investigate, through a mixed-method approach, the motivations and strategies of the instructors of Udemy and Skillshare. Semi-structured interviews and a quantitative analysis of the characteristics of Skillshare’s courses, obtained using a Web scraper, suggest that while a significant proportion of the marketplace’s instructors are outreach driven, the majority are income driven. They develop strategies to maximize their revenues, notably by adapting the characteristics of their courses, such as the number of videos, to the business model of the platform. Courses are shorter on Skillshare than on Udemy, where instructors’ incomes are proportional to the number of registrations. We hypothesize that the latter platform’s business model incentivizes instructors to create longer courses in order to attract wider audiences.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".