Bibliographic record
Abstract
Electronic writing competes with the spoken word to such an extent that adolescents are abandoning traditional writing forms, such as letter writing, in the favour of digital forms, such as blogs and wikis ( Penloup & Joannidès, 2014 ). In this digital era, schools are facing two daunting challenges: incorporating technology in second language teaching and learning writing. This chapter reports on a knowledge synthesis funded by the Canadian Social Sciences and Humanities Council. The scope of the original study included the use of technologies in both first- (L1) and second-language (L2) classes; in this chapter, however, only findings related to L2 classes will be presented. This knowledge synthesis pursued three specific objectives: (a) to take the stock of digital forms of writing studied via Canadian and international scientific research between 2005 and 2020, (b) to identify studies on using digital technologies to teach and learn writing, and (c) to synthesize and assess the impacts digital technologies have on written texts and the writing process. The findings of this knowledge synthesis are particularly relevant, because the arrival of new technologies has changed the environment in which digital writing is practised and, although many studies have been carried out regarding the impacts of these new writing practices, there is a void of rigorous knowledge syntheses that allow for the better comprehension of these impacts. We expect that teachers, decision makers, and researchers will benefit from the results of this knowledge synthesis, which maps forms of digital writing, compares research conclusions, and identifies promising research priorities.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".