Do instructor beliefs and attitudes matter? Understanding associations between beliefs, attitudes, and practices
Bibliographic record
Abstract
A mixed-method approach is used to examine the relationship between university instructors’ practices in blended courses and their epistemological and pedagogical beliefs, and their attitudes toward technology. The study draws from a socio-constructive perspective and uses Fishbein and Ajzen’s belief and attitude theory and applies it to a blended framework. Data were collected using an online survey of 71 instructors, semi-structured individual interviews with 24 instructors and one to four classroom observations of 15 instructors. The interviews were audio-recorded, and the classroom observations were collected via hand-written notes. Data were analyzed via NVIVO 12 and SPSS. Findings explore relationships between instructors’ beliefs around knowledge, hard work and student self-regulation skills to their instructional strategies and involvement of students. The study also looks at instructor use of different technologies in blended courses and explores the connection between their attitudes towards technology and their practices. This study, thus, offers guidance to academic leaders, instructors, developers, and policymakers and has several implications for research and instructor development workshops.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".