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Record W2492255273 · doi:10.5539/ies.v9n8p134

Exploring the Relationship between Writing Apprehension and Writing Performance: A Qualitative Study

2016· article· en· W2492255273 on OpenAlexvenueno aff
Kamal J I Badrasawi, Ainol Madziah Zubairi, Faizah Idrus

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsApprehensionPsychologyMathematics educationQualitative researchThematic analysisAcademic writingCornerstonePedagogySociology

Abstract

fetched live from OpenAlex

<p class="apa">Writing skill is seen as a cornerstone of university students’ success in both academic and career life. This qualitative study was conducted to further explore the teachers’ and students’ perceptions on the relationship between writing apprehension and writing performance, contributing factors of writing apprehension, and strategies to reduce writing apprehension. Semi-structured interviews were conducted to get more in-depth information from two respondents: one experienced instructor of teaching writing at the Centre for Languages and Pre-University Academic Development (CELPAD), International Islamic University Malaysia, and another, a graduate student who was reported to having a high level of writing apprehension using Daly and Miller’s (1975) questionnaire on writing apprehension. Thematic analysis approach was used for data analysis. Both respondents were convinced that writing apprehension has a negative influence on students’ writing performance; the sources of contributing factors could be students, instructors, and teaching learning setting; and writing apprehension could be reduced through suggested strategies. It is recommended that instructors should be more aware of students’ problems in the writing skill.</p>

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.490
GPT teacher head0.522
Teacher spread0.032 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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