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Record W4308896207 · doi:10.5539/elt.v15n12p14

Is TikTok an Effective Technology Tool in English Vocabulary Expansion?

2022· article· en· W4308896207 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnglish Language Teaching · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyPsychologyEnglish vocabularyMathematics educationEnglish languageVocabulary developmentLanguage acquisitionVocabulary learningPositive attitudeTeaching methodComputer scienceLinguisticsSocial psychology

Abstract

fetched live from OpenAlex

In recent years, technology has had a huge impact on learning and teaching processes. TikTok is a technology tool that is prominently used by young adults. Vocabulary is the most essential component of a language, and learners can improve their vocabulary via this attractive tool. This paper investigates the attitudes of Saudi secondary school learners towards using TikTok videos in English vocabulary learning and how TikTok videos contribute to expanding English language vocabulary in Saudi secondary school learners. The study uses a quantitative method to collect data using a survey questionnaire. The participants consist of 50 secondary school students. The outcomes illustrate that students have a positive attitude towards using TikTok in learning English vocabulary. Moreover, students note that TikTok offers various features and options that assist in expanding English vocabulary.

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.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.009
GPT teacher head0.319
Teacher spread0.310 · 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