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Record W2596511830

The Rights and Responsibility of Test Takers when Large-Scale Testing Is Used for Classroom Assessment

2017· article· en· W2596511830 on OpenAlexaffvenue
Christina van Barneveld, Karieann Brinson

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsLakehead University
Fundersnot available
KeywordsTest (biology)Expectancy theoryScale (ratio)PsychologyMathematics educationDescriptive statisticsSocial psychologyPedagogyStatisticsMathematicsGeography
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this research was to identify conflicts in the rights and responsibility of Grade 9 test takers when some parts of a large-scale test are marked by teachers and used in the calculation of students’ class marks. Data from teachers’ questionnaires and students’ questionnaires from a 2009–10 administration of a large-scale test of Grade 9 mathematics were analyzed using descriptive statistics. Written comments by teachers were analyzed into themes. Results were interpreted using a framework comprised of a policy document and two theories that are relevant to large-scale testing: the rights and responsibility of test takers as documented in the Standards for Educational and Psychological Testing (AERA/APA/NCME/JCSEPT, 2014), expectancy-value theory of motivation, and theory regarding the developmental stage of students in Grade 9. Several conflicts were identified. Potential solutions to conflicts were presented.

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)0.131
metaresearch head score (Gemma)0.427
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.693

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1310.427
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.011
Scholarly communication0.0070.006
Open science0.0030.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.138
GPT teacher head0.402
Teacher spread0.264 · 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 designNot applicable
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
Published2017
Admission routes2
Has abstractyes

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