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Record W4310350731 · doi:10.5539/elt.v15n12p114

Teacher and Student Perceptions of CALL Feedback: Synchronous and Asynchronous Teacher Electronic Feedback in EFL-Writing at King Saud University

2022· article· en· W4310350731 on OpenAlexvenueno aff
Sahar Nasser Alshumaimmeri, Reem Alqarni

Bibliographic record

VenueEnglish Language Teaching · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyContext (archaeology)Peer feedbackPerceptionRealmMathematics educationBlackboard (design pattern)Asynchronous communicationPedagogyLikert scaleComputer-mediated communicationHigher educationThe InternetComputer science

Abstract

fetched live from OpenAlex

In the realm of education, feedback from peers and teachers is of critical importance. The rapid growth of electronic feedback (EF) has led to various innovations in feedback, particularly for teachers’ EF (TEF) in the context of English as a foreign language (EFL). This study contributes to the literature on feedback by examining how teachers and students in EFL writing classes were influenced by TEF offered synchronously via online class discussion, and TEF offered asynchronously via Blackboard. This study focuses on both teachers’ perceptions (elicited from interviews) and students’ perceptions (elicited from surveys) regarding their respective experiences of TEF. The study uncovered three main findings; most of the TEF addressed the content, organization, and language structure of the students’ EFL writing, and the teachers adjusted their TEF according to students’ needs and course objectives; teachers and students considered TEF to be effective and accessible.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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