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Record W2289233763 · doi:10.18806/tesl.v32i0.1219

Test de Francais Laval-Montreal: Does It Measure What It Should Measure?

2016· article· en· W2289233763 on OpenAlexvenueaboutno aff
Romain Schmitt, Shahrzad Saif

Bibliographic record

VenueTESL Canada Journal · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTest (biology)Construct validityTask (project management)Test validityConstruct (python library)PedagogySocial psychologyPsychometricsDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

This article reports on a study conducted as part of a larger investigation of the predictive validity of the Test de Français Laval-Montreal (TFLM), a high-stakes French language test used for admission and placement purposes for Teacher- Training Programs (TTPs) in major francophone universities in Canada (Schmitt, 2015). The objective of this study is to examine the validity of TFLM tasks for measuring language abilities required by tasks common to the Target Language Use (TLU; Bachman & Palmer, 2010) domains in which preservice teachers are expected to function. Adopting Messick’s conception of construct validity (1989) and Bachman & Palmer’s Framework of Task Characteristics (2010), the study features a comprehensive task analysis detailing the characteristics of TFLM tasks in contrast to those of three major TLU academic and instructional contexts linked to the test. The results of the study are discussed in terms of the standards of validity (Messick, 1996) and qualities of usefulness (Bachman & Palmer, 1996). Findings suggest that TFLM tasks and constructs do not represent those of the TLU contexts and do not address the language needs of preservice teachers as identified by the Ministère de l’Éducation, du Loisir et du Sport (MELS). The implications for the consequential aspect of TFLM validity and the potential nega- tive consequences of TFLM use as an admission test are discussed. Cet article présente une partie d’une étude plus complète sur la validité prédictive du Test de Français Laval-Montréal (TFLM), test de langue française à enjeux critiques utilisé comme test d’admission et de placement dans les programmes de formation initiale en enseignement d’importantes universités francophones au Canada (Schmitt, 2015). Le but de ce e étude est d’analyser la validité des tâches du TFLM à des fins d’évaluation des compétences linguistiques exigées dans les tâches communes aux domaines d’utilisation de la langue cible dans lesquels les enseignants en formation doivent fonctionner (Target Language Use (TLU); Bachman & Palmer, 2010). Basée sur la conception de la validité conceptuelle de Messick (1989) et le cadre d’analyse des caractéristiques des tâches de Bachman & Palmer (2010), l’étude compare de manière détaillée les tâches du TFLM à celles de trois contextes académiques et pédagogiques d’emploi de la langue cible. Les résultats de cette analyse sont évalués en termes de validité (Messick, 1996) et des qualités des tests (Bachman & Palmer, 1996). Les résultats indiquent que les tâches du TFLM et les construits qu’il est sensé évaluer ne correspondent pas à ceux des contextes d’emploi de la langue cible et ne répondent pas aux besoins des ensei- gnants en formation tels qu’identi és par le Ministère de l’Éducation, du Loisir et du Sport (MELS). La validité du TFLM, les conséquences ainsi que les aspects potentiellement négatifs de son utilisation comme test d’admission sont discutés.

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 imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.113
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.799

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.113
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0020.004
Scholarly communication0.0040.004
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.035
GPT teacher head0.231
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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Same venueTESL Canada JournalSame topicEFL/ESL Teaching and LearningFrench-language works237,207