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Record W3046875880 · doi:10.5539/ijel.v10n5p265

Fostering Saudi EFL Learners’ Communicative, Collaborative and Critical Thinking Skills Through the Technique of In-Class Debate

2020· article· en· W3046875880 on OpenAlexvenueno aff
Fahmeeda Gulnaz

Bibliographic record

VenueInternational Journal of English Linguistics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCritical thinkingClass (philosophy)PedagogyMathematics educationComputer science

Abstract

fetched live from OpenAlex

Diverse learning styles of the learners require instructors to utilize wide variety of instructional strategies to engage their interest and motivation. In-class debate is a systematic instructional strategy utilized to bring the dynamics of social life into the classroom to cultivate learners’ active-involvement in the content of the subject. It hones learners’ range of skills, such as; oral-communication, social-interaction, critical thinking skills and internalization of the course content. For the purposes of present study, the researcher utilized qualitative cum quantitative research tools to collect the data from the participants. An opinionnaire with 14 items was developed with closed ended questions. The instrument was designed to measure four variables, which are tabulated into four sections (See tables 3-6): (a) in-class debate inculcates learners’ collaborative, communicative and cooperative paradigms;(b) it triggers learners’ complete mastery of the course content, i.e., retention, assimilation and understanding, coupled with boosts stack of skills involved in the process; (c) it fosters EFL learners’ ability to critically evaluate and analyze everything; and (d) finally it focuses on learners’ creative and critical thinking skills and make them active and independent pursuers of knowledge. The survey was administered to 87 female EFL learners of Taif University to identify the impacts of debate on their range of academic skills. The findings of the study indicate that in-class debate effectively generates learners’ several skills since it targets their thinking skill, contemplation, reflection and ultimately stimulates their productive, receptive, analytical and critical thinking skills. Yet, more faculties can be activated and maximum results can be obtained, if this interactive strategy is implemented in a structured format with a properly designed rubric focusing on learners’ specific skills.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.042
GPT teacher head0.379
Teacher spread0.337 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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