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Record W4409835840 · doi:10.59944/postaxial.v3i2.442

Culturally Responsive Assessment and Evaluation Practices in Multilingual Classrooms

2025· article· en· W4409835840 on OpenAlex
Ria Lopush

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

VenueInternational Journal of Post Axial Futuristic Teaching and Learning · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLinguisticsPsychologyComputer scienceMathematics educationNatural language processing

Abstract

fetched live from OpenAlex

This study explores the implementation of culturally responsive assessment and evaluation practices in multilingual classrooms. It aims to examine how educators adapt their assessment strategies to accommodate the cultural and linguistic diversity of their students. The research highlights the importance of making assessments more inclusive and equitable, ensuring that all students have an equal opportunity to demonstrate their learning. Through qualitative methods, including interviews, classroom observations, and document analysis, the study identifies the types of culturally responsive assessments used by teachers, the challenges they face, and the impact of these practices on student engagement and academic performance. The findings suggest that culturally responsive assessments enhance students' motivation, participation, and perceptions of fairness. However, challenges such as inadequate training, limited time, and a lack of institutional support remain. The study concludes that culturally responsive assessment practices have the potential to significantly improve educational outcomes, but require ongoing support and professional development for teachers to be fully effective.

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)0.006
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.009
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 integrity0.0000.001
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 head0.030
GPT teacher head0.456
Teacher spread0.426 · 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