Bibliographic record
Abstract
Despite the increasing research on the benefits of using corpora in language teaching and learning, Data-Driven Learning (henceforth, DDL) research has been criticized for its lack of contribution to second language theories. This paper intends to address this gap by examining the assumptions of Involvement Load Hypothesis (ILH) using two DDL tasks with different cognitive loads. Learners were assigned to one of two conditions: reading only or translation. Based on ILH, translation is more effective than reading in learning vocabulary, as it induces more cognitive involvement (Laufer & Hulstjin, 2001). The two groups received a pretest to ensure their unfamiliarity with six target words. Each group underwent one instructional session under one of the two conditions. After the session, students took three immediate post tests on the six target items: active recall of form, passive recall of meaning, and production. Contrary to the expectations of ILH, the results of the immediate post tests showed no statistically significant difference in the mean of vocabulary knowledge between the two groups. In addition, in the delayed test, the reading-only group showed statistically higher scores in the active recall of form than their translation peers. The findings highlight some important theoretical and pedagogical implications for using DDL tasks, particularly for EFL vocabulary learning.
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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 | 0.021 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".