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Record W2776268142 · doi:10.5539/ies.v11n1p44

The Effectiveness of Using Online Blogging for Students’ Individual and Group Writing

2017· article· en· W2776268142 on OpenAlexvenueno aff
Hashem A. Alsamadani

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyElectronic publishingMathematics educationAudience measurementCurriculumCollaborative writingCitizen journalismPedagogyThe InternetComputer scienceWorld Wide WebAdvertising

Abstract

fetched live from OpenAlex

The current research study investigates the effectiveness of online blogging for students’ individual and group writing skills. The participants were divided into individual learners and group learners. They produced pre-writing and post-writing samples through blogging practices. The study conducted lasted for 14 weeks so that blogging could be optimized. The results of the study reveal that unlike traditional ways of improving writing skills, blogging has revolutionized EFL pedagogy and methodology (learning and teaching). Blogging-based writing practice is more participatory and interactive in that learners can dramatically improve their writing skills in terms of content, word choice, style, language mechanics and the like. The learner-blogger becomes aware that the arbiter is no longer the classroom teacher, the audience or readership. This study recommends that blogging be part of writing classes and be incorporated into school curricula. This essentially requires pedagogical consideration of the design of blogging-based writing materials.

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)0.005
metaresearch head score (Gemma)0.035
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.119
GPT teacher head0.471
Teacher spread0.353 · 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

Citations56
Published2017
Admission routes1
Has abstractyes

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