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Record W4385721572 · doi:10.1080/20004508.2023.2244136

Conceptions of classroom assessment and approaches to grading: teachers’ and students’ perspectives

2023· article· en· W4385721572 on OpenAlexafffundabout
David Baidoo-Anu, Amirhossein Rasooli, Christopher DeLuca, Liying Cheng

Bibliographic record

VenueEducation Inquiry · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of AlbertaQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGrading (engineering)PsychologySituational ethicsMathematics educationAccountabilityPedagogySocial psychology

Abstract

fetched live from OpenAlex

Classroom assessment and grading play central roles in education, with important impacts on teachers and students. This study examined the interactions between Canadian teachers’ and students’ conceptions of assessment and approaches to grading. 219 teachers and students completed a survey with two scales: Teachers’ Conceptions of Assessment (TCOA) and Teachers’ Approaches to Grading (TAG). Factor analysis of the TCOA scale showed four factors: assessment to improve teaching and learning, negative or irrelevant assessment, assessment for student and school accountability, and inaccurate assessment. Analysis of the TAG survey also showed four factors: social-emotional pressures for grade increases/changes, situational considerations for grade increases/changes, contextual-based grading, and achievement-based grading. Discriminant analysis showed that four out of these eight factors from the two scales had the strongest effects on teachers’ and students’ membership in their respective groups. The results contribute to a more complete understanding of assessment cultures as conceived by teachers and students.

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)0.018
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 score0.515
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0060.015
Scholarly communication0.0110.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.250
GPT teacher head0.462
Teacher spread0.212 · 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

Citations16
Published2023
Admission routes3
Has abstractyes

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