Learner Variables in Second Language Listening Comprehension: An Exploratory Path Analysis
Bibliographic record
Abstract
Listening comprehension plays a key role in language acquisition, yet little is known about the variables that contribute to the development of second language (L2) listening ability. This study sought to obtain empirical evidence for the impact of some of the learner variables and the degree to which they might predict success in L2 listening. The learner variables of interest included: first language (L1) listening ability, L1 vocabulary knowledge, L2 vocabulary knowledge, auditory discrimination ability, metacognitive awareness of listening, and working memory capacity. Data from 157 Grade Seven students in the first year of a French immersion program indicated a significant relationship among most of the variables and L2 listening ability. A number of path analyses were then conducted, based on hypothetical relationships suggested by current theory and research, in order to uncover relationships between the variables in determining L2 listening comprehension ability. The best fit to the data supported a model in which general skills (auditory discrimination and working memory) are initially important, leading to more specific language skills (L1 and L2 vocabulary) in determining L2 listening comprehension. In positing a provisional model, this study opens up useful avenues for further research on model building in L2 listening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".