Bibliographic record
Abstract
This article reports on the results of an action-research project carried out with agroup of first year university students from an “English Program” at a public university in Bogotá. The project aimed to gain insights into EFL writing and to analyze the role that feedback played in the process of writing. The experience was implemented through the interaction of two groups of students, one from Colombia and the other from Canada, who interacted regularly by means of using blogs. Students were provided with three different spaces: a personal blog in which they wrote about topics of personal interest, a group blog that allowed students to work and write cooperatively, and a debate blog that required the use of argumentative writing. The findings of this research suggest that EFL writing is greatly developed when students feel part of a community to interact with and to share similar interests and language learning goals which are mediated, in this case, by technology. It was also found that by writing in blogs students not only developed their writing but more importantly, students have the possibility to portray and show their own selves through the written pieces they post. Finally, the feedback was found to be beneficial in EFL writing as it was a crucial ingredient that gave student-writers tools to scaffold in the writing process.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".