University Instructors Beliefs, Attitudes, and Practices Across Stem and Non-Stem Blended Courses
Bibliographic record
Abstract
The rising trend of blended learning in higher education has benefited student engagement and learning across all disciplines. Universities have facilitated and encouraged teaching and learning centers to offer technology-based professional development workshops and help to instructors, yet blended instructional practices do not always fall in line with the anticipated success of these new initiatives. It is suggested that instructors' blended practices across disciplines are influenced by their epistemological beliefs (about the nature of knowledge), pedagogical beliefs (conceptions of teaching and learning), and their attitudes towards technology in blended courses. These relationships between instructors' beliefs, attitudes, and practices may vary further across disciplines. \nThis mixed-methods study shed light on and explored the relationship among instructors' beliefs, attitudes, and practices in several ways and compared and contrasted them across STEM and non-STEM instructors. Using a socio-constructive and socio-cultural framework and drawing from Fishbein and Ajzen's belief and attitude theory, this study explores the relationship between instructors' epistemological and pedagogical beliefs, their attitudes towards technology and their blended practices. A survey with seventy instructors teaching blended courses in a university in Southwestern Canada was conducted using the online survey platform Qualtrics. Semi-structured in-depth interviews were conducted with twenty-four instructors across STEM and non-STEM blended course, followed by one to four classroom observations of fifteen instructors. Findings show how instructors' epistemological and pedagogical beliefs are related to their practices and how their attitudes towards technology intertwine within their beliefs and practices. Additionally, this study offers implications for universities investing in blended courses and STEM and non-STEM instructors and designers for blended courses.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".