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Record W4385632515 · doi:10.53103/cjlls.v3i4.104

The Influence of Oral Communication on the Learning Engagement of Students

2023· article· en· W4385632515 on OpenAlexvenueno aff

Bibliographic record

VenueCanadian Journal of Language and Literature Studies · 2023
Typearticle
Languageen
FieldComputer Science
TopicEnglish Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyDescriptive statisticsPearson product-moment correlation coefficientSample (material)Medical educationData collectionMathematics educationMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

The study entitled "The Influence of Oral Communication to the Learning Engagement of the Students" aims to determine the significant influence of oral communication on the learning engagement of students in English. The researchers conducted a survey using a descriptivecorrelational research design. The data were gathered from a sample size of 197 respondents using the validated modified questionnaire. The researchers interpreted the data using statistical tools such as Mean, Pearson Product-Moment Correlation and Simple Linear Regression. According to the findings, the level of oral communication of the students is good. The result indicated that oral communication significantly influenced the student's learning engagement. Furthermore, it showed that oral communication for the students should be interactive to boost their learning engagement. The researchers emphasized the need for future researchers to conduct other researches using learning engagement topics not included in the current study.

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)0.003
metaresearch head score (Gemma)0.029
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.303
Teacher spread0.285 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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