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Record W2620093162 · doi:10.5539/elt.v10n6p174

Second Language Writing and Assessment: Voices from Within the Saudi EFL Context

2017· article· en· W2620093162 on OpenAlexvenueno aff
Rana Obeid

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyContext (archaeology)Likert scaleMathematics educationFeelingScale (ratio)English as a foreign languageEnglish languagePedagogyForeign language

Abstract

fetched live from OpenAlex

This small scale, quantitatively based, research study aimed at exploring one of the most debated areas in the field of Teaching English to Speakers of Other Languages (TESOL); and that is, the perceptions and attitudes of English as a Foreign Language (EFL) teachers as well as EFL learners at an English Language Institute (ELI) at a major university in the Western region of Saudi Arabia, King Abdulaziz University, towards second language writing assessment. The research study involved, randomly selected twenty-two EFL teachers and seventy-eight EFL students between the period of September 2016 and December 2016. Two, purposefully designed, twenty-item, Likert scale questionnaires were distributed amongst the teachers and students. One for the participating EFL teachers and one for the participating EFL students. Data analysis using descriptive statistical methods indicated several concerns which EFL teachers and students have with regards to the writing assessment in general and to the obstacles EFL teachers face when teaching and assessing writing. In addition, there was an indication of general resentments and strong feelings amongst the EFL students where the majority indicated that they are sometimes graded unfairly and writing assessment should take another, more holistic approach rather a narrow one. The study makes recommendations for future research.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, 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 score0.224
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0030.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.019
GPT teacher head0.284
Teacher spread0.265 · 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

Citations30
Published2017
Admission routes1
Has abstractyes

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