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Record W4390711382 · doi:10.36312/jolls.v3i2.1209

Investigating Students’ Writing Skills in Generating Descriptive Texts: Experiences Learned from English for Specific Purposes (ESP) Contexts in Privates Universities

2023· article· en· W4390711382 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Language and Literature Studies · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGrammarPsychologyVocabularyCategorizationComprehensionMathematics educationDescriptive statisticsLinguisticsPedagogy

Abstract

fetched live from OpenAlex

Writing occupies a paramount role in the conveyance of ideas, demanding a profound comprehension of the nuances integral to crafting impeccable written compositions. The research under consideration was undertaken with the objective of delving into the writing skills of students and discerning the challenges they encounter in composing descriptive texts. Employing a mixed-method approach, the study engaged English learners at STIE AMM Mataram, constituting a cohort of 28 students. To attain a comprehensive understanding of their writing abilities and difficulties, the research employed writing tests and interviews as research instruments. The research outcomes revealed a mean score of 69.92 for the students, indicative of a concerning trend of inadequate proficiency in writing descriptive texts, essentially categorizing their abilities as 'poor.' A closer examination of the data delineated specific performance distributions among the students: 12% garnered scores below 60, designating them as 'poor,' while 40% fell within the 61-70 score range, also categorized as 'poor.' Moreover, 36% secured scores in the 71-80 range, positioning them in the 'average' category, and a mere 12% earned scores designated as 'very good. Vocabulary and organization stood out with ratings categorized as 'good to average,' showcasing relative strengths. Conversely, content, grammar, and mechanics were characterized by a 'fair to poor' categorization, underscoring significant areas of difficulty. In particular, students grappled with challenges concerning grammar, content development, and mechanical aspects of writing. In light of these findings, it is evident that students encounter multifaceted difficulties, particularly in the realms of grammar, content creation, and mechanics. As a viable solution, it is recommended that English teachers prioritize providing students with ample opportunities for writing practice. These opportunities should be designed to specifically enhance content development and grammar proficiency in writing descriptive texts.

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.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.041
GPT teacher head0.351
Teacher spread0.310 · 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