Faculty Use of Established and Emerging Technologies in Higher Education: A Unified Theory of Acceptance and Use of Technology Perspective
Bibliographic record
Abstract
Our effectiveness as instructors lies ultimately in how well our students can understand and apply the concepts we teach. In response to the growing importance of accountability in the educational process and the abundance of social networking technology and communication tools available for possible classroom use, this paper will use The Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the adoption of established and emerging information technology in higher education classrooms. Hence, the goal of this paper is to test theoretical explanations from UTAUT in the context of higher education through the development of a set of hypotheses predicting the conditions under which classroom technology use is likely to emerge. Data collection occurred via an online survey. The instrument was sent to business faculty members teaching face-to-face classes at a southeastern university. Our findings suggest that in the context of instructors’ use of technology for classroom purposes, the most important antecedents are performance expectancy, effort expectancy, social influence, and habit with more complex effects when gender is added as an interaction term. Results from this study will provide useful information on the frequency of use of technology, along with significant factors affecting its adoption in the classroom. Departmental leaders interested in the variations in individual faculty’s level of inclination toward technological changes would find them particularly useful.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".