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Record W4322768167 · doi:10.1080/00220973.2023.2183375

Investigating the Role of Academic, Social, and Emotional Self-Efficacy in Online Learning

2023· article· en· W4322768167 on OpenAlex
Sungjun Won, Meg Kapil, Brodie J. Drake, Rikka A. Paular

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

VenueThe Journal of Experimental Education · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSelf-efficacyPsychologyAcademic achievementPath analysis (statistics)Coronavirus disease 2019 (COVID-19)Social psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Despite increased social and emotional challenges in online learning during the COVID-19 pandemic, little attention has been paid to students’ social and emotional self-efficacy beliefs. The present study investigated university students’ (N = 268) academic, social, and emotional self-efficacy beliefs as predictors of their academic achievement, sense of belonging, and well-being in online learning during the pandemic. We first evaluated the factor structure of the three types of self-efficacy. Results revealed that academic, social, and emotional self-efficacy beliefs were related yet distinct constructs. In the path model, gains in academic self-efficacy positively predicted students’ academic achievement, whereas social self-efficacy and emotional self-efficacy positively predicted students’ sense of belonging and well-being, respectively. In addition, students’ mastery experience emerged as a significant predictor of longitudinal changes in academic self-efficacy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.081
GPT teacher head0.470
Teacher spread0.389 · 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