Digital performance learning: Utilizing a course weblog for mediating communication
Bibliographic record
Abstract
Two sections of university-level technical writing courses were given an authentic task to write an article for publication for an outside stakeholder. A quasi-experimental study was conducted to determine the differences in learning outcomes between students using traditional writing methods and those using social media to generate articles. One section was randomly assigned to follow the traditional writing process using computer- mediated writing and small group peer workshops of paper drafts, while the other section published its work-in- progress on a course blog and engaged in web-mediated online collaboration to determine if there are meaningful differences between computer-mediated and web-mediated writing as measured by learning outcomes in terms of publication rates and grades. The results of this study demonstrate that utilizing an online social network in the form of a course blog positively impacted learning outcomes; however, a close examination of the published peer review feedback on the course blog indicated a moderately negative relationship between the quality of the feedback received and acceptance scores. Thus, the value of the web- mediated workshop was not based on the outcome of the workshop, but rather on having providing feedback, which generated a higher level of engagement and more time spent on task as compared to the paper draft workshop section.
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.
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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".