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Record W4408534011 · doi:10.5430/wjel.v15n4p327

Comparative Analysis of Feedback Practices and Perspectives in Online Academic Writing Assessments at Two Regional Tertiary Institutions

2025· article· en· W4408534011 on OpenAlexvenueno aff
Emad A. Alawad, Fatma Hamid

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceAcademic writingTertiary levelTertiary careMathematics educationPolitical sciencePsychologyMedicine

Abstract

fetched live from OpenAlex

This study addresses the gap in comparative analyses of feedback strategies in online writing assessments at higher education institutions in the Gulf region. Although previous research has examined different facets of online feedback, direct institutional comparisons have been overlooked. This research provides new insights into effective feedback techniques through a comparative study of feedback approaches at the Modern College of Business and Science (MCBS) in Oman and Qassim University (QU) in Saudi Arabia. The primary aim is to investigate faculty practices and perceptions regarding online feedback, evaluating its effectiveness and impact on student success. By examining how faculty members provide online feedback and its effects on students' achievements, the study seeks to enhance students' academic writing skills and mastery of disciplinary knowledge. The findings contribute to the conversation on effective feedback strategies in contemporary education, offering valuable insights for improving feedback practices, informing faculty development, guiding institutional policies, and enriching the broader literature on online teaching and assessment. The study's conclusions were based on data gathered from 41 respondents and analyzed through quantitative and qualitative methods, providing a comprehensive understanding of online feedback practices and their impact on enhancing EFL students’ writing skills.

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)0.001
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 score0.250
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.081
GPT teacher head0.459
Teacher spread0.379 · 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
Published2025
Admission routes1
Has abstractyes

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