Orientations to English academic language learning among Chinese high school students in a technology-supported learning environment in Canada
Bibliographic record
Abstract
This study identifies the orientation to English academic language learning among ESL students in a technology-supported learning environment, by investigating Chinese students' intuitive negotiation of the components of their ESL instruction that they considered most effective in the Canadian educational setting. Twenty-four Chinese ESL students and five ESL teachers in a high school in Toronto participated in the study. A mixed factorial design, with one between-subject variable (student groups) and three within-subject variables (time, conditions, test versions), investigated the impact of technology-supported scaffolding on vocabulary acquisition. Repeated-measures ANOVAs assessed the students' vocabulary learning in two reading conditions across two English proficiency groups. Descriptive and qualitative analysis of questionnaires, interviews with teachers and students, and classroom observations established students' strategy profiles. Students subconsciously adopted a resource-orientated functional approach in search of strategies to optimize vocabulary learning by taking advantage of available resources. They predominately used bilingual resources to compensate for the mismatch between their preferred learning strategies and those promoted within their classroom ESL instruction. The students acquired more vocabulary with technology-supported scaffolds that were congruent with their preferred learning strategies than they did under non-technology-supported learning conditions. The rate of students' vocabulary acquisition related to their L2 proficiency. Significant variations were found between students' understanding of word meanings in English and Chinese across conditions and groups. Further research is recommended to examine the distinction between generic learning strategy frameworks (such as that proposed by Oxford, 1990) and the specific culture-based strategies that students actually utilize. Such generic frameworks did not adequately capture important aspects of students' learning strategies in the present study. Also, it may be useful to explore the potential benefits of providing a structured set of obligatory scaffolds within a technology-supported environment as opposed to letting students choose from a variety of available scaffolds. The latter approach may work well for more advanced learners; for beginners a more structured tutorial route may be advantageous.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".