Sociocultural Theory, the L2 Writing Process, and Google Drive: Strange Bedfellows?
Bibliographic record
Abstract
This article draws on sociocultural theory and the process approach to writing as familiar and widely used elements of second language pedagogy that can be leveraged in interesting new ways through the use of digital technology. The focus is on a set of affordances offered by Google Drive, a popular online storage and document-sharing technology. On the assumption that dynamic collaboration with peers, teacher feedback, and authentic computer-mediated tasks contribute to the development of writing skills, Google Drive can be seen as an effective tool for meaningful and cost-effective technology-enriched instruction that entails po- tential advances in pedagogical practice. An overview of the app’s user-friendly interface is provided from a nonspecialist perspective, followed by a discussion of how one may go about organizing a writing course using the tool in a language lab or online. Specific examples of tasks used in ESL courses at a North American postsecondary institution are included. Cet article puise dans la théorie socioculturelle et l’approche à l’écriture par pro- cessus, des concepts bien connus et répandus en enseignement L2 et dont on peut tirer parti de façons innovatrices grâce à la technologie numérique. Notre étude porte sur les capacités de Google Drive, une technologie populaire qui permet le stockage en ligne et le partage de documents. Tenant comme acquis que la colla- boration dynamique avec les pairs, la rétroaction de la part des enseignants et les tâches authentiques assistées par ordinateur contribuent au développement des compétences en écriture, nous présentons Google Drive comme un outil tech- nologique efficace et rentable qui vient enrichir la pédagogie de l’enseignement authentique. Nous offrons un aperçu de l’interface conviviale de l’application du point de vue d’un non-spécialiste pour ensuite discuter de la façon d’organiser, dans un laboratoire d’informatique ou en ligne, un cours d’écriture avec Google Drive. Des exemples spécifiques de tâches employées dans des cours d’ALS dans une institution post-secondaire en Amérique du Nord sont présentés.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".