Revision and Participation Patterns in Grades 5 and 6 Wiki Writing
Bibliographic record
Abstract
Our study examined middle grade students’ participation in wikis during their two-month social studies unit co-taught by two teachers as part of a larger action research project. Using an analysis of 42 grades 5 and 6 students working together in eight wiki writing groups, we report on the frequency and types of revisions they made to collaboratively-written essays, and the distribution of the workload across group members in each of the wiki groups. Discussion data with 16 students from these wiki groups helps contextualize our analysis.Our findings suggest that given their extended time to write, students revised frequently, making replacements more often than they deleted, added or moved content. Students indicated a willingness to change others’ contributions and to have their own contributions revised by others in order to improve the quality of the essays. The majority of their revisions were at the word level, rather than at sentence, paragraph, and whole-text levels. One student in each group contributed significantly more frequently than any other group member. There were no gender or grade patterns in the frequencies or types of contributions that students made to the wikis.
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".