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Record W2998551013 · doi:10.5539/ijel.v10n1p255

EFL Students’ Perception of Classroom Assessment Environment in Translation Courses

2019· article· en· W2998551013 on OpenAlexvenueno aff
Fahad Saad Alsahli

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionPsychologyClass (philosophy)Mathematics educationLearning environmentMedical educationPedagogyMedicineComputer science

Abstract

fetched live from OpenAlex

The aim of this study was to explore the students’ perceptions on classroom assessment environment in translation courses. The sample of this study was made of 341 participants studying at an English language department in a Saudi university. Data were collected using self-reported questionnaire which was designed based on Alkharusi’s (2011) scale. Factor analysis was computed and the results revealed the presence of Alkharusi’s two original factors: perceived learning-oriented, and perceived performance-oriented classroom assessment environments. T-test was employed to explore the differences in perceptions between male and female students, but no significance was found between them. Implications and recommendations for classroom assessment as well as for future research have also been discussed. The practical implication of the research is that student outcomes might be improved by establishing classrooms that match those educational environments which have been shown to be associated with students’ learning. A limitation of most classroom learning environment instruments is that they measure an individual student’s perceptions of a whole class, as distinct from students’ perceptions of their own roles in the classroom. It is likely that future classroom and school environment research will be enhanced if personal as well as group assessments are adopted.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.279
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.024
GPT teacher head0.368
Teacher spread0.344 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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