Understanding the Relationship Among Self-efficacy, Utility Value, and the Community of Inquiry Framework in Preservice Teacher Education
Bibliographic record
Abstract
School closures during the COVID-19 pandemic have shown the importance of distance education, and teachers have been tasked with designing and delivering online courses in a short amount of time without much preparation or deliberation. As the future generation of teachers, preservice teachers need to be prepared to teach online, and their motivation to do so is a key factor in how successfully they do it. The community of inquiry framework provides researchers and practitioners with a framework for designing and delivering online courses, while self-efficacy and utility value are important motivational constructs predicting future engagement and success in tasks. In this cross-sectional survey study, we investigated preservice teachers’ (n = 344) perceptions of their self-efficacy, utility value, the importance of the three components of the community of inquiry framework: teaching presence, social presence, and cognitive presence. Our results show that overall, preservice teachers had high motivation to teach online and high perceptions of the three presences. Our regression analyses indicated that while preservice teachers’ self-efficacy was a significant predictor of teaching presence, utility value only significantly predicted social presence. We discuss the implications of these findings for teacher education programs, including a holistic approach to teaching online learning and instructional design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".