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Record W1578913844

What Has Experience Got to Do with It? An Exploration of L1 and L2 Test Takers' Perceptions of Test Performance and Alignment to Classroom Literacy Activities.

2011· article· en· W1578913844 on OpenAlexaffabout
Christine Doe, Liying Cheng, Janna Fox, Don A. Klinger, Ying Zheng

Bibliographic record

VenueePrints Soton (University of Southampton) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsCarleton UniversityQueen's University
Fundersnot available
KeywordsTest (biology)Graduation (instrument)LiteracyPsychologyPerceptionMathematics educationScale (ratio)PedagogyEngineering
DOInot available

Abstract

fetched live from OpenAlex

The importance of first language (L1) and second language (L2) test takers’ experience with large-scale literacy testing has been well documented in educational research. Our study focused on the Ontario Secondary School Literacy Test (OSSLT), a crosscurricular literacy test that is one of the graduation requirements for Ontario high school students. We drew on qualitative data obtained from open-ended questions on a largescale survey administered to OSSLT test takers. Whether the test takers were preparing to take the test or had already taken the test seemed to play a critical role in L1 and L2 test takers’ perceptions. The most salient results highlight the role that test experience had on test takers’ perceptions of their test performance and the alignment between the test and their classroom literacy activities.

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)0.010
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.054
GPT teacher head0.292
Teacher spread0.237 · 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 designQualitative
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

Citations10
Published2011
Admission routes2
Has abstractyes

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