Lexical decision tests for foreign language placement at the post-secondary level
Bibliographic record
Abstract
This study examines the use of a lexical decision test as a placement measure in a university Spanish foreign language program. Lexical decision tests measure word recognition and have been shown to correlate well with other language proficiency tests. The main advantage of using lexical decision as a placement tool is its ease of creation and administration. We examined the ability of lexical decision to discriminate between adjacent placement levels in our program and found that it functions up to the low-intermediate level only. This limitation may be due to the way vocabulary is taught, the stages of development of the lexicon, and the learning plateau that intermediate learners appear to reach. Nonetheless, lexical decision allows for a quick initial sorting of students into different levels and reduces the need for individual placement by instructors. Cette étude examine l’usage d’un test de décision lexicale comme mesure de placement pour un programme universitaire d’espagnole comme langue étrangère. Les tests de décision lexicale sont une mesure de la reconnaissance des mots et ils ont montré une bonne corrélation avec d’autres tests de compétence linguistique. L’avantage principale que la décision lexicale offre comme outil de placement est la facilité de création et d’administration. On a examiné la capacité de la décision lexicale pour distinguer entre des niveaux de placement contigus et on a trouvé qu’elle fonctionne jusqu’au niveau bas intermédiaire seulement. Cette limitation peut être attribuée à la manière dont le vocabulaire est enseigné, aux étapes de développement du lexique, et au plateau d’apprentissage que les apprenants intermédiaires semblent atteindre. Néanmoins, la décision lexicale nous permet de trier les étudiants selon leur niveau d’une manière préliminaire et rapide et elle réduit le besoin de placement individuel par les instructeurs.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".