Sadownik, S.A. (2022). Correlations on PeppeR for Time Spent Online with Online Activity by Graduate Students.pdf
Bibliographic record
Abstract
Many academics and early career professionals are tasked with assessing online contributions in graduate courses in education. Various methods suggest research has considered the use of rubrics and attempted to assess the level of critical thinking presented in online discussions, course design, assignment weighting, engagement in course material, relationships between peers, language learners and different subject specialists approaches to discussion forums and in some cases interpretative flexibility of the course and technology. In this correlational study, two data sets and four hypotheses were tested with the use of a correlational matrix generator: (H1) The “Time Online” will have a statistically significant positive correlation with the “Words Written” for both data sets; (H2) The “Time Online” will have a statistically significant positive correlation with the “Notes Written” for both data sets; (H3) The “Time Online” will have a statistically significant positive correlation with the “Replies” for both data sets; (H4) The “Time Online” will have a statistically significant positive correlation with the “Notes Read” for both data sets. Results suggest one data set presented statistically significant positive correlation in each category (p < 0.01) while the other data set only presented one statistically significant category (p < 0.05) for “Notes Written”.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".