The Evolution of Vocabulary Learning Strategies in a Computer-Mediated Reading Environment
Bibliographic record
Abstract
Numerous studies have indicated that the provision of appropriate computer-mediated support to second language (L2) learners results in different vocabulary learning outcomes. However, there is no study available that investigates the transition in their way of learning vocabulary under the influence of technology-based support. This article presents a comparative study that examines the differences between L2 learners’ use of vocabulary strategies with or without such support. Twenty-four ESL students in a Toronto high school were involved. A language learning system was implemented to facilitate a technology-enhanced reading environment. Observations and tape-recorded field notes contribute to the data collection. The results showed that (a) a variety of strategies were employed across cognitive, compensatory, metacognitive and social categories when students learned vocabulary through sustained reading within the computer-mediated environment and that (b) significant variations in the techniques and functionalities of strategies were found between the two reading conditions. Situated within the vocabulary learning strategy framework, the article argues that the technology-enhanced scaffoldings can effectively assist students to advance their learning strategies, potentially optimizing their reading-based vocabulary acquisition.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".