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Record W4386224051 · doi:10.5539/elt.v16n9p102

Focuses and Trends of the Research on Task-based Language Teaching (1998-2022)

2023· article· en· W4386224051 on OpenAlexvenueno aff
Ping Yu

Bibliographic record

VenueEnglish Language Teaching · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)Language educationSecond-language acquisitionField (mathematics)Computer scienceMathematics educationForeign languageLanguage assessmentEmpirical researchTask analysisLanguage acquisitionPsychologyLinguistics

Abstract

fetched live from OpenAlex

Task-based language teaching, as a communicative language teaching model, has gradually become a hot topic in the field of second language teaching and acquisition. In order to present the research focuses and trends of task-based language teaching, this paper, by resorting to the Web of Science and Excel, conducts a qualitative and quantitative statistical analysis of the relevant papers published in ten internationally renowned second language acquisition academic journals from 1998 to 2022. The results indicate that: (1) the number of papers presents a dynamic upward trend; (2) the research subjects are mainly college students who speak English as a second or foreign language; (3) the main research fields cover task performance, task characteristics, task implementation conditions, learner internal factors and integration of TBLT and computer network; (4) with research methodology being more diversified, empirical studies take a dominant position and the quantitative method plays a leading role. The results of this study have some implications for future task-based language teaching and research.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
GPT teacher head0.370
Teacher spread0.338 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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