edX participants’ profile: analysis of the factors that lead to the search for certification
Bibliographic record
Abstract
Massive Online Open Courses (MOOCs) are freely accessibleonline courses with open registration. This term was first coined in 2008when professors of University of Manitoba (Canada) started an onlinecourse free and open to anyone. In 2012, two platforms were launched, EdXand Coursera. Until now, these two platforms remain as the most popularMOOCs providers in the world attracting universities from all of thecontinents. The present study performs data analysis of Harvard and MITcourses available in EdX during the first four years of operation. Theobjective was to understand students’ and courses’ profiles and the factorsthat make certifications more attractive to the participants. This paper couldidentify some factors that contribute to students' motivation in obtainingformal certification. It was important to see that variables related toengagement impact in the inclination to obtain a certification. Furthermore,demographical characteristics as sex and age are relevant so that institutionscan focus on specific targets.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".