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Record W4403929858 · doi:10.5539/ijel.v14n6p132

Investigating English Majors’ Online Learning Anxiety and Its Influencing Factors

2024· article· en· W4403929858 on OpenAlexvenueno aff
Yujie Wang, Lianrui Yang, Hongshan Yin

Bibliographic record

VenueInternational Journal of English Linguistics · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyPsychologyMathematics educationOnline learningBusinessComputer scienceMultimedia

Abstract

fetched live from OpenAlex

Anxiety in second language acquisition refers to the tension or unease that students experience while learning a foreign language. Despite extensive research on anxiety in second language acquisition, little has been done to explore online learning anxiety. This study aimed to examine Chinese English majors’ anxiety and its influencing factors in online learning from the control-value theory perspective. Through a utilization of questionnaires and semi-structured interviews, this study found: (1) Chinese English majors exhibited a moderate level of online learning anxiety, with gender and academic year level exerting no significant influence on anxiety levels; (2) Multiple influencing factors, involving the individual learners, their teachers and peers as well as the learning environment, contributed to Chinese English majors’ online learning anxiety. It is suggested that teachers prioritize increasing communication and interaction with students in online teaching to alleviate the anxiety, boost the learning effect and promote the physical and mental well-being of the students.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.319
Teacher spread0.292 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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