L2 Spanish vocabulary teaching in US universities: Instructors’ beliefs and reported practices
Bibliographic record
Abstract
Studies on teachers’ beliefs about vocabulary learning and teaching have focused, so far, on English as a second language (L2), or foreign language (FL), in different contexts but little attention has been given to other L2s and FLs. In this study, 15 Spanish L2 instructors at large universities were interviewed in order to better understand where they stand when it comes to (1) the importance they give to vocabulary, as compared to grammar, in their classes, (2) how they decide which words to teach, and (3) how they assess students’ word knowledge. These interviews were subsequently analysed following Grounded Theory. Most instructors declared favoring grammar over vocabulary in their courses because the former is seen as more challenging and useful than the latter and because institutional practices and materials also present such a preference. When it comes to vocabulary selection, most of them declared feeling insecure in their decisions due to lack of access to useful resources and to vocabulary goals not being stated clearly anywhere in the syllabi. This lack of clarity when it comes to vocabulary learning goals also results in doubts about the usefulness of even evaluating word learning at all and an overreliance on informal assessments.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".