The Effect of Evaluation Factor on the Incidental Vocabulary Acquisition through Reading
Bibliographic record
Abstract
This empirical study investigates the respective effectiveness of three factors (need, search and evaluation) included in task-induced involvement load on the EFL vocabulary learning and retention. Three tasks with the same amount of involvement load but containing different factors are assigned to 108 non-English majors at Beijing Institute of Petrol-chemical Technology in China. After these reading tasks, the participants are given an unannounced immediate posttest. One week later, the participants are given the delayed posttest. A 3 × 2 analysis of variance (ANOVA) is employed to process the scores and identify the relationship between the EFL incidental vocabulary learning and the three factors contained in the involvement loads. The results are assumed to show that the Evaluation factor is more decisive and crucial than the other two factors (need and search). Learners benefit more by using the target words in their original contexts. That means vocabulary instruction should focus on tasks that require high degrees of evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".