Covid 19 and Education” The Untold Story of the Barriers to Technology Adoption From A Tertiary Viewpoint
Bibliographic record
Abstract
The Covid- 19 Pandemic has changed the way Higher learning institutions normally conduct learning. Educational technology is a learning tool that helps lecturers enhance learning through instructional practices; however, lecturers are having difficulties adopting technology. The purpose of this study was to examine the barriers that lecturers’ face when attempting to adopt technology and the support needed to be successful within their instructional practices. The conceptual frameworks for this study were Bandura’s self-efficacy theory and Rogers’ diffusion of innovation. The study included eight lecturers from a Caribbean college in Antigua and Barbuda as participants. Data were collected through interviews and analyzed using open coding and thematic analysis. Findings from the study indicated that there were barriers that were preventing lecturers from adopting technology. These barriers included the need for professional training, institutional support, and observational learning of others which would assist with lecturers’ pedagogy, content knowledge, and technology adoption. The results of the study may lead to social change by revealing potential barriers that lecturers face during technology use. The study can also provide both lecturers and stakeholders with data that is Caribbean-specific and can provide the most effective plan to support lecturers’ adoption of technology. Keywords: Barriers to Technology Adoption, Caribbean lecturers’ hindrances to technology adoption, Covid-19, Barriers to technology adoption in Higher Learning DOI: 10.7176/JEP/12-10-02 Publication date: April 30 th 2021
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".