Bibliographic record
Abstract
In response to increasing interest in Vygotskian sociocultural theory in second-language learning (Lantolf and Thorne, 2006; Swain, Kinnear, and Steinman, 2015) and the call for understanding language-learning processes in relation to contexts surrounding individuals (e.g., Polio and Williams, 2009; Ferris and Hedgcock, 2014), this study adopts a sociocultural approach – more specifically, an activity theory (Leont’ev, 1981) framework – to explore an undergraduate student’s approach to L2 writing in a preparatory writing course. Using a single case study design (Duff, 2014), I investigated how a student from China learned to write academic papers that met the academic norms in an English as a second language (ESL) writing class in an American university. Specifically, I analyzed how his writing activity aligned with his instructor’s proposed approach to a writing task. Through the analysis of course materials, the participant’s written work, observations, email communications, and interviews, I tracked how his agency (Bhowmik, 2016; Casanave, 2012; Lee, 2008; Saenkhum, 2016) as a writer developed over his first semester in the ESL program. Findings indicate that while the participant did not follow the operations assigned by the instructor, he acted strategically to accomplish selected parts of his writing assignments. His mediated actions were driven by his goals and motives that were understood from within his social and cultural environments, and interacted with each other in a dynamic and constructive manner. Overall, the study underscores the need for flexible approaches to writing instruction and the usefulness of employing activity theory as a framework in studying L2 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 | 0.030 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.082 |
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".