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Record W4362698963 · doi:10.5430/wjel.v13n5p269

Effect of Anxiety and Self-Efficacy on Class Performance in Arabic Language Online Class

2023· article· en· W4362698963 on OpenAlexvenueno aff
Suo Yan Mei, Morufudeen Adeniyi Shittu, Suo Yan Ju

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyClass (philosophy)ArabicDescriptive statisticsPsychologySelf-efficacyComputer scienceMathematics educationSocial psychologyArtificial intelligenceStatisticsLinguisticsMathematics

Abstract

fetched live from OpenAlex

This study aimed to explore the effect of anxiety and self-efficacy on class performance in Arabic language online classes. This study used a quantitative research approach, and the data was collected through a survey instrument administered to 148 first-year Arabic learner students enrolled in an Arabic language online class. The data were analysed using descriptive statistics as well as a structural equation model to examine the relationship between variables. The study’s results showed that anxiety had a negative effect on class performance, while self-efficacy positively impacted class performance. Therefore, anxiety is the main contributor to Arabic online learning performance. Additionally, self-efficacy was found to have a moderating effect on the relationship between anxiety and class performance. The findings of this study suggest that anxiety and self-efficacy are important factors to consider in an online learning environment, as they can significantly impact class performance. Based on the results of this study, it is recommended that educators in online learning environments implement strategies to enhance self-efficacy and reduce anxiety levels among their 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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.313
Teacher spread0.304 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2023
Admission routes1
Has abstractyes

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