How MOOC-Takers Estimate Learning Success: Retrospective Reflection of Perceived Benefits
Bibliographic record
Abstract
Massive open online courses (MOOCs) have attracted a great deal of interest in recent years as a new learning technology. Since MOOCs inception, only limited research has been carried out to address how learners perceive success in MOOCs after course completion. The aim of this study was to investigate the perceived benefits as the measurement of learning success. Narrative interviews were conducted with 30 Russian-speaking learners who completed at least one MOOC in full. By employing text analysis of interview transcripts, we revealed the authentic voices of participants and gained deeper understanding of learners' perceived benefits based on retrospective reflection. The findings of the study indicate that after finishing MOOCs, learners have received tangible and intangible benefits that in general justified their expectations. University-affiliated students, as well as working professionals, recognized the complementarity of MOOCs, but their assessments were limited to educational tracks. We discovered that taking MOOCs often coincided with the time when an individual was planning to change career, education, or life tracks. The results of the study and their implications are further discussed, together with practical suggestions for MOOC providers.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".