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Record W3109853812 · doi:10.5539/elt.v13n12p65

Complexities of Writing Skill at the Secondary Level in Bangladesh Education System: A Quantitative Case Study Analysis

2020· article· en· W3109853812 on OpenAlexvenueno aff
Sujana Suvin

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsParagraphPsychologyVocabularyGrammarMathematics educationConversationAction researchData collectionPedagogyMedical educationSociologyComputer science

Abstract

fetched live from OpenAlex

The goal of this study is to examine difficulties in and outside the classrooms which are the real obstacles to the writability of teaching and learning in the secondary level Bangladesh system. The secondary level tests writing skills by summary, paragraph, letter, application, story, conversation, composition, report, e-mail etc. Students who study at the high school level face serious writing complexities. The major problems in the examination are the vocabulary and grammar complexities. In this study, the reasons for the complexities of writing skills were examined. This study was conducted at Jahangirnagar School & College, Savar, Dhaka to find out the strategies for writing skills. Important questions of research have been developed to identify writing skill complexities. Data were taken from teachers, students and parents based on questionnaire survey. Teachers and students were encouraged to participate actively in the survey. Following collection of data, the practical advice for students and teachers was analyzed. While skills at secondary schools have been evaluated, most teachers are not taking any action to evaluate the abilities of students. Students are also less interested in writing skill in practice. The aim of the study is therefore to show a new image to the writing skill at the secondary level Bangladesh education system.

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)0.002
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 score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.056
GPT teacher head0.370
Teacher spread0.314 · 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 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

Citations19
Published2020
Admission routes1
Has abstractyes

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