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Record W2982693524 · doi:10.5539/ijel.v9n6p309

High School Students’ Common Errors in Writing Essays

2019· article· en· W2982693524 on OpenAlexvenueno aff
Vũ Phi Hổ Phạm, Do Thi Phuong Trinh

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsWriting processPsychologyVerbMathematics educationPedagogyComputer science

Abstract

fetched live from OpenAlex

Students’ writing problems have become one of the first and foremost issues in writing classrooms, and to know the common errors which frequently occur on students’ writing papers is usually what the writing teachers have conducted in the classrooms. However, no research study has been conducted at Huynh Man Dat High school to investigate this aspect. The purpose of the current study is to investigate the common written errors on students’ writing essays and their attitudes towards the writing process approach. The target subjects are from grade twelve, Huynh Man Dat (HMD) High School. Data collection was from writing tasks and questionnaires. The study found that the four most common errors frequently occur in students’ writing journals are relating to tenses, collocations, spellings, and verb forms. The findings from an attitude questionnaire indicated that the students expressed positive attitudes towards it and showed a high appreciation for the effects of process writing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.296
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2019
Admission routes1
Has abstractyes

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