Video screen capture to document and scaffold the L2 writing process
Bibliographic record
Abstract
This chapter explores the potential of video screen capture (VSC) as a technology that can provide new insights when investigating learner-computer interactions in CALL research, and that can play a mediating role in second language (L2) writing pedagogy. Arguments are put forward as to why CALL researchers and language educators should be interested in this accessible and flexible tool. Three studies are described to consolidate these arguments. The first one, a usability study, investigates L2 learners’ dictionary search processes in the context of the design of an online dictionary prototype. The second study examines the composition processes and strategies of L2 writers. The third study looks at the pertinence and added value of integrating VSC in the L2 writing class. Affordances of VSC arose from these studies. VSC emerged as a powerful documentation tool enabling the collection of process-oriented learner data and new forms of dynamic corpora. It also emerged as a retrospection tool capable of supporting L2 writers in their literacy development and as a scaffolding tool to provide multimodal feedback on L2 written output.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".