The Impact of Wikis & Videos Integration Through Cooperative Writing Tasks Processes
Bibliographic record
Abstract
ICT role in education nowadays is not only important, but also effective; its advancement allows a vast opportunity to be explored by EFL teachers into the EFL classroom. This action-research study envisioned and carried out from our teaching practice basis with English language B1 level students at Weisheit institute. Observation and instruments Implementation stages determined the positive impact of the integration of Wikis in EFL classrooms and how cooperative writing processes eased and helped the students improve their writing performance. Indeed, taking into account as a main strategy the “ICT” as a tool to improve teaching practices. This research was conducted through mixed-method approach and included a methodical process through data collection of journals, pre and post writing tests, semi-structured interviews and aptitude test. Of course, by looking upon that the application of these instruments helped us identify certain points of particular interest providing self-reflection on our own teaching-learning processes regarding as main problems; lack of writing skills, lack of vocabulary, grammar mistakes and writing inaccuracy. The strategies implemented had to do mainly with the integration of Wiki websites as a pedagogical instrument to improve writing skills through pre-writing eye-catching elements such as videos implementation in order to trigger motivational writing processes.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".