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Record W2530098379

What are the Learning Values of Grades? Exploring Grading Policies in Canada and China

2016· article· en· W2530098379 on OpenAlexaboutno aff
Liying Cheng, Christopher DeLuca, Heather Braund, Yi Mei, Adelina Valiquette, Yan Wei, Deyu Xing

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsGrading (engineering)InternationalizationChinaChristian ministryPolitical scienceGlobalizationAcademic achievementImmigrationMathematics educationPedagogyPsychologyBusiness
DOInot available

Abstract

fetched live from OpenAlex

The current trend towards globalization, immigration, and internationalization of schools and universities around the world has led to the increased use of grades across educational systems. Given the use of grades for student promotion, mobilization, and admission into educational programs internationally, there is an urgent need to understand the validity of grades––the alignment of grading policies, practices, values, and consequences––within and across learning contexts. This study specifically investigates the learning value embedded within grading policies across two educational contexts – Canada and China. This analysis of Ministry of Education documents within and acros s the two learning contexts provides unique insights into grading policies from four provinces in Canada and central grading policies in China. This comparative analysis indicates significant differences in policies guiding teacher constructed grades in the two learning contexts. In Canada, achievement is the primary consideration in the construction of classroom grades, whereas grades in China include considerations of both learning and the learner. The findings of the study have implications for understanding the validity of grade interpretations of student achievement across educational systems.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.162

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.320
Teacher spread0.256 · 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 teacher head, 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

Citations0
Published2016
Admission routes1
Has abstractyes

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