Bibliographic record
Abstract
<p class="3">Since it was first introduced in 2008, Massive Open Online Courses (MOOCs) have been attracting a lot of interest. Since then, MOOCs have emerged as powerful platforms for teaching and learning academic writing. However, there has been no detailed investigation of academic writing MOOCs. As a result, much uncertainty still exists about the differences of writing MOOCs compared with traditional types of writing instruction in the classroom. Drawing on historical emphases in writing instruction, five approaches are illustrated: skills, creative writing, process, social practice, and a socio-cultural perspective. This study uses data from six academic writing MOOCs to examine what approaches are revealed within their writing instructions. Focusing on a group of six academic writing MOOCs at college level, attributes and features of writing MOOCs were explored by analyzing syllabi, video lectures, and assignments. Overall, the study found that these academic writing MOOCs stick to a traditional model of teaching writing, “writing as skills.” These findings suggest that instructors who teach academic writing through online platforms showed that their immediate concerns were not a social practice or socio-cultural context. Rather, teaching and learning of grammatical accuracy and surface features of texts at college level appear to be best purpose of academic writing MOOCs.</p>
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".