To What Extent Does the Involvement Load Hypothesis Predict Incidental L2 Vocabulary Learning? A Meta‐Analysis
Bibliographic record
Abstract
Abstract The involvement load hypothesis (ILH) was designed to predict the effectiveness of instructional tasks for incidental L2 vocabulary learning. In this meta‐analysis we examined 398 effect sizes from 42 empirical studies ( N = 4,628) to explore (a) the overall predictive ability of the ILH, (b) the relative effects of different components of the ILH (need, search, and evaluation), and (c) the influence of potential factors moderating learning (e.g., time on task, frequency of encounters or use, and test format). Results showed that the ILH was significantly predictive of learning and explained 15.0% and 5.1% of the variance in effect sizes on immediate and delayed posttests, respectively. We found that the evaluation component contributed to the greatest amount of learning, followed by need, whereas search did not contribute to learning. Moderator analyses revealed that (a) test format and frequency moderated learning gains and (b) involvement load had a greater impact on learning than time on task.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.030 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".