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Assessing Integrated Skills

2013· other· en· W1549299204 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsActive listeningReading (process)Relevance (law)ConventionTest (biology)PsychologyVocational educationLiteracyValue (mathematics)Cognitive skillCognitionMathematics educationCognitive psychologyPedagogyComputer scienceLinguisticsSociologyCommunication

Abstract

fetched live from OpenAlex

A recent trend in language assessment has been to assess examinees’ abilities to integrate source reading or listening material into their writing and speaking performance in ways that simulate the cognitive, communication, and literacy demands of real‐life academic or vocational tasks. This chapter describes the justifications for this innovation in interactionist theories of communication, cognition, and assessment; through appeals to authenticity and relevance; and as a means to counter the effects of testing methods. The chapter explains how and why integrated skills assessment differs from the convention of assessing writing, speaking, reading, and listening as separate skills but also produces unique complications for measurement, test design, and examinee performance. Future directions are proposed to refine the constructs and tasks guiding integrated skills assessment, to increase their value for diagnostic purposes, and to verify their proposed benefits for teaching, learning, and high stakes decision making such as university admissions or employment.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.138
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1420.004

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.

Opus teacher head0.027
GPT teacher head0.379
Teacher spread0.352 · 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

Quick stats

Citations28
Published2013
Admission routes1
Has abstractyes

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