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Record W4385240403 · doi:10.5430/wjel.v13n7p243

Peer- and Self-Assessment in Primary School English Language Classrooms

2023· article· en· W4385240403 on OpenAlexvenueno aff
Mazidah Mohamed, Norizan Abdul Razak, Wahiza Wahi

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsReading (process)English languagePeer assessmentMathematics educationPeer feedbackPsychologySelf-assessmentLanguage assessmentPedagogyMedical educationMedicineLinguistics

Abstract

fetched live from OpenAlex

Peer- and self-assessment could benefit the pupils in learning English language as a second language due to the aspects of essential whole group and individual reflections within the practice. This paper intends to investigate the practices of peer- and self-assessment among the national primary school English language teachers in a district in Selangor, Malaysia via a mixed methods approach. A survey was administered on 244 teachers, followed by interview and classroom observation on eight subset participants. From the survey, approximately 93% of the respondents had an emerging practice of peer- and self-assessment in their English language classrooms, which happened 50% of the time. The interview and observation findings show that the teachers needed extra time to train the pupils for the practice of peer- and self-assessment, mostly on written work, but also applicable during reading and speaking lessons. Pupils could apply self-assessment by knowing their levels and what to be done, sometimes based on worksheets and checklists. Sometimes, peer- and self-assessment were difficult and confusing among the pupils with lower English language proficiency levels. In the classroom, the pupils needed more time to receive guidance and training from the teachers in order to practise peer- and self-assessment, despite not fully in English. This implies that the teachers were attempting to enact peer- and self-assessment among the pupils, albeit rather deviating from the target language at times.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.161
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.309
Teacher spread0.300 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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