Faculty Perceptions of Online Education and Technology Use Over Time: A Secondary Analysis of the Annual Survey of Faculty Attitudes on Technology from 2013 to 2019
Bibliographic record
Abstract
Research on faculty use of technology and online education tends to be cross-sectional, focusing on a snapshot in time. Through a secondary analysis of the annual Survey of Faculty Attitudes on Technology conducted by Inside Higher Ed each year from 2013 through 2019, this study investigated changes in faculty attitudes toward technology and online education over time. Specifically, the study examined and synthesized the findings from surveys related to attitudes toward online education, faculty experiences with online learning, institutional support of faculty in online learning, and faculty use of technology. Results showed a low magnitude of change over time in some areas (e.g., proportion of faculty integrating active learning strategies when converting an in-person course to a hybrid/blended course) and a large magnitude of change in other areas (e.g., proportion of faculty who believe that online courses can achieve the same learning outcomes as in-person courses). These results reveal that, prior to the widespread shift to remote and online learning that occurred in 2020 because of the COVID-19 pandemic, faculty perceptions of technology and online learning were static in some areas and dynamic in others. This research contextualizes perceptions towards online learning prior to the pandemic and highlights a need for longitudinal studies on faculty attitudes toward technology use going forward to identify factors influencing change and sources of ongoing tension.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".