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
Big data from massive open online courses (MOOCs) have enabled researchers to examine learning processes at almost infinite levels of granularity. Yet, such data sets do not track every important element in the learning process. Many strategies that MOOC learners use to overcome learning challenges are not captured in clickstream and log data. In this study, we interviewed 92 MOOC learners to better understand their worlds, investigate possible mechanisms of student attrition, and extend conversations about the use of big data in education. Findings reveal three important domains of the experience of MOOC students that are absent from MOOC tracking logs: the practices at learners’ workstations, learners’ activities online but off-platform, and the wider social context of their lives beyond the MOOC. These findings enrich our understanding of learner agency in MOOCs, clarify the spaces in-between recorded tracking log events, and challenge the view that MOOC learners are disembodied autodidacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.006 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it