Factors that influence e-learning adoption by international students in Canada
Bibliographic record
Abstract
In recent years, the global higher education sector is increasingly adopting technology enhanced learning as part of the drive towards innovation in teaching and learning. In order to design and implement a successful e-learning environment, educational providers need to understand students' perceptions of e-learning. This pilot study examines the factors that affect students' intentions to adopt e-learning, namely individual user differences, perceived ease of use, perceived flexibility of use and student satisfaction. Data were collected from 151 international students studying in a Canadian public college via an online questionnaire. Multiple regression analyses revealed that the best predictor of student satisfaction was perceived ease of use, while the best predictor of intention to adopt was perceived flexibility of use. An examination of individual user differences such as gender, age, education, frequency of use and previous e-learning experience revealed that only frequency of use emerged as a predictor of intention to adopt e-learning. The findings provide important insights for colleges and higher educational institutions that are interested in the design and delivery of e-learning courses that would lead to student satisfaction and higher e-learning adoption rates. Further discussion and implications are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".