Case Studies of Implementing Writing Courses Online in Higher Education
Bibliographic record
Abstract
Reports of online writing courses at universities provide isolated experiences rather than multiple-case comparisons. This study uses activity theory to explore the nature of successful developments of four online writing courses in higher education. Universities desire online learning to meet strategic and accessibility needs. Faculty may lack skills and resources but administrators can provide supportive environments. Online learning risks higher dropouts and simplistic pedagogies, but effective design encourages productive interactions and ongoing course improvements. Six reported cases described online writing courses that either preserved classroom writing pedagogies, or addressed systemic dysfunctions in classroom courses. This qualitative study uses a convenience sample of four case studies recruited from online university courses in technical or professional writing in the United States and Canada. Information was collected through interviews with instructors and available personnel, course walkthroughs and artifacts. Cases were analyzed using activity theory. Cases consisted of undergraduate and graduate courses in technical or educational writing, legislative drafting, and proposal writing. The courses were ongoing activity systems constrained by professor experience and source materials, but subject to structural tensions that resulted in expanded motivations for access, achievability, and community integration. Stakeholders can recognize the impact on design from the reason the course was requested, professor independence, existing course materials, and ongoing measurement. The small sample was suitable for generating theory but not statistical generalization. Future research can explore courses in other countries and languages, writing disciplines, and institutions.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".