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Record W2132378253 · doi:10.3138/cmlr.64.1.039

Research on Integrated Performance Assessment at the Post-Secondary Level: Student Performance Across the Modes of Communication

2007· article· en· W2132378253 on OpenAlexvenueno aff
Eileen W. Glisan, Daniel Uribe, Bonnie Adair‐Hauck

Bibliographic record

VenueCanadian Modern Language Review/ La Revue canadienne des langues vivantes · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPresentational and representational actingInterpersonal communicationForeign languageMathematics educationPsychologyComputer sciencePedagogyLinguisticsSocial psychology

Abstract

fetched live from OpenAlex

Abstract: This article reports on a performance-based assessment research project conducted at the US Air Force Academy during the 2004–2005 academic year. The primary purpose of the research project was to measure post-secondary students’ progress towards meeting the Standards for Foreign Language Learning in the 21st Century – specifically the three modes of communication. The article explains how Integrated Performance Assessment measures learners’ language performance in light of current research in foreign language assessment. The paper demonstrates a sample Integrated Performance Assessment for a post-secondary Spanish culture and civilization course, and shares data on the students’ performance for the interpretive, interpersonal, and presentational modes of communication. Correlational data on the impact of middle and high school language learning on post-secondary language performance is also shared. Finally, the paper argues that, unlike traditional testing formats, integrated performance assessment connects teaching, learning, and assessment seamlessly.

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)0.012
metaresearch head score (Gemma)0.031
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 score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.001
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 head0.068
GPT teacher head0.348
Teacher spread0.280 · 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

Citations29
Published2007
Admission routes1
Has abstractyes

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Same venueCanadian Modern Language Review/ La Revue canadienne des langues vivantesSame topicEFL/ESL Teaching and LearningFrench-language works237,207