Intended use of educational technology after the COVID-19 pandemic
Bibliographic record
Abstract
Use of educational technology has escalated in recent times. This study surveyed teachers in the business school of a Canadian university after it quickly pivoted to online delivery of all classes after the initial jolt of the COVID-19 pandemic. The purpose of the survey was to investigate the antecedents of the teachers’ intention to use educational technology in the post-pandemic period. Consistent with the theory of reasoned action (TRA), attitude and subjective norm are both positively associated with respondents’ behavioural intention to use educational technology within the two years following the pandemic. The subjective norm construct positively influences respondents’ perceptions of the usefulness of the technology, indicating that respondents are influenced by their co-workers’ opinions and the communications transmitted by the institution. Technological complexity and perceived usefulness of the technology respectively negatively and positively impact attitude, indicating that teachers perform an internal cost-benefit analysis when contemplating using educational technology. Technological complexity can also be considered an opportunity cost as it negatively impacts perceived usefulness. Lastly, facilitating conditions negatively impact technological complexity, indicating that supportive resources are important. These results should be of interest to university policymakers seeking to increase the use of educational technology.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".