Toward Standards for Reporting Research: A Review of the Lliterature on Computer-Supported Collaborative Learning
Bibliographic record
Abstract
We conducted a meta-review of the computer supported collaborative learning (CSCL) literature. This literature included a rich array of methodologies, theoretical and operational definitions, and collaborative models. However, the literature lacked an overall framework for reporting important design and research details. This paper highlights key findings from our systematic review. The paper: (a) presents the array of definitions, tools, and supports researched in the CSCL literature and (b) proposes standards for reporting collaborative models, tools, and research. These standards, which have implications for both the CSCL and computersupported collaborative work areas, have potential to build a shared language upon which cross-disciplinary communication and collaboration may be based.
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 | 0.507 | 0.642 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.058 | 0.059 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".