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Record W3130144609 · doi:10.5539/jel.v10n2p84

Exploring the Influence of Teacher-Student Interaction on University Students’ Self-Efficacy in the Flipped Classroom

2021· article· en· W3130144609 on OpenAlexvenueno aff
Lin Li, Shan-Shan YANG

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsnot available
FundersChengdu UniversityMinistry of Education of the People's Republic of China
KeywordsFlipped classroomPsychologyBlended learningPreferenceMathematics educationSelf-efficacyPedagogyEducational technologySocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study is to explore the impact of teacher-student interaction on undergraduate students’ self-efficacy in a Chinese university setting. Students came from natural science, management, economics, medicine, engineering and humanities. The empirical results demonstrate that teacher-student interaction has positive impact on students’ self-efficacy and their preference of the flipped classroom. Furthermore, the positive relationship between teacher-student interaction and students’ self-efficacy is partially mediated by students’ preference of the flipped classroom. Educators should focus on student-centered learning and motivate students’ preference of the flipped classroom. Students should be encouraged to actively participate in the flipped learning as well. It contributes to the reform of the flipped classroom and improvement of teaching quality in the universities.

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)0.011
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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.093
GPT teacher head0.422
Teacher spread0.329 · 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

Citations57
Published2021
Admission routes1
Has abstractyes

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