Effects of internal and external attentional manipulations and working memory on second language vocabulary learning
Bibliographic record
Abstract
In light of mixed findings in existing input enhancement research, Issa and Morgan-Short in a 2019 article urged researchers to compare the relative effects of input enhancement that taps into learners’ attention to the external format of second language (L2) target forms (e.g. through capitalizing or boldfacing the forms) and input enhancement that taps into learners’ attention to the internal attributes of L2 target forms (e.g. via increasing the frequency of the forms). In response to this call, the study described in this article drew on a pretest-treatment–posttest-experimental-design to explore whether working memory (WM) capacity modulates the extent to which L2 learners benefit from input enhancement engaged by internal and external attentional manipulations for partially-acquired L2 vocabulary. Analyses of these learners’ lexical gains under different experimental conditions showed that although compound input enhancement engaged by internal attentional manipulations did indeed lead to better lexical gains, such manipulations did not unequivocally lead to greater gains than the external manipulations in all cases. Furthermore, simple input enhancement engaged by internal attentional manipulations (i.e. varying the contextual supports for the target words) could be as effective as compound input enhancement. Importantly, we found that the aforementioned pedagogical effects of internal and external manipulations were both modulated by differences in WM capacity, albeit to differing extents. Insights from this study provide important pedagogical implications for differentiated input enhancement theory and practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".