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Record W4392148326 · doi:10.47766/idarah.v7i2.1981

YouTube and English Learning: Transformative Impact on Self-Regulated Learning Competencies

2023· article· en· W4392148326 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIdarah (Jurnal Pendidikan dan Kependidikan) · 2023
Typearticle
Languageen
FieldComputer Science
TopicEnglish Language Learning and Teaching
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsTransformative learningMetacognitionCompetence (human resources)PsychologyMathematics educationCognitionSelf-regulated learningPedagogySocial psychology

Abstract

fetched live from OpenAlex

The awareness of learning English has risen not only on students but also general community. However, expecting to master English merely relies on classroom learning or formal setting are considered not sufficient. Therefore, more English learners both students and public figured out how to study by themselves. The emergence of YouTube has changed the way people learn English. The various kinds of videos and their flexibility of access raised their awareness to learn English individually. It is a qualitative study. The data were analysed based on (Miles et al., 2014) theory. The researchers interviewed three participants to collect the data. They were selected based on the criteria managed by the researcher. This study used three competencies of self-regulated learning (SRL): cognitive, motivation and metacognitive. This study found that YouTube positively impacted SRL. The participants proved that YouTube helped them to understand English better, made their learning fun and enabled them to reflect their English competence.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.003
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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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