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Record W2800734114 · doi:10.5539/elt.v11n5p116

The Impact of Wikis & Videos Integration Through Cooperative Writing Tasks Processes

2018· article· en· W2800734114 on OpenAlexvenueno aff
Lubin Fernando Franco-Camargo, Gonzalo Camacho Vásquez

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyGrammarPsychologyAction researchMathematics educationTest (biology)Process (computing)Teaching methodTechnology integrationPedagogyComputer scienceLinguistics

Abstract

fetched live from OpenAlex

ICT role in education nowadays is not only important, but also effective; its advancement allows a vast opportunity to be explored by EFL teachers into the EFL classroom. This action-research study envisioned and carried out from our teaching practice basis with English language B1 level students at Weisheit institute. Observation and instruments Implementation stages determined the positive impact of the integration of Wikis in EFL classrooms and how cooperative writing processes eased and helped the students improve their writing performance. Indeed, taking into account as a main strategy the “ICT” as a tool to improve teaching practices. This research was conducted through mixed-method approach and included a methodical process through data collection of journals, pre and post writing tests, semi-structured interviews and aptitude test. Of course, by looking upon that the application of these instruments helped us identify certain points of particular interest providing self-reflection on our own teaching-learning processes regarding as main problems; lack of writing skills, lack of vocabulary, grammar mistakes and writing inaccuracy. The strategies implemented had to do mainly with the integration of Wiki websites as a pedagogical instrument to improve writing skills through pre-writing eye-catching elements such as videos implementation in order to trigger motivational writing processes.

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)0.005
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.427
Teacher spread0.390 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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