Bibliographic record
Abstract
The education sector is not really averse to emerging technology, including the Internet. Technology-Enhanced Learning has evolved into a field of study and practice centered on the use of information and communication technologies in teaching and learning. This research aimed to analysis the use of Moodle for course creation as E-learning platform at selected private institutions in Canada. The study took place at chosen private institutions in Canada. To support the research to evaluate use of Moodle for course creation as E-learning platform at private institutions, the researchers used four Moodle creation aspects, first instructors’ technology experience, second was university’s system quality, third was information quality and last was instructors ‘internet experience. The study used a survey to assess the current study using a quantitative analysis approach. The data was collected at random among 78 instructors from Canada's private institutions. The findings revealed that Instructors’ internet experience as the use of Moodle element has significant positive influence on course creation at 5% level. Furthermore, all beta value is higher than .001. All models have very high adjusted R2 (0.681, 0.627, 0.712, and 0.732 respectively) indicating the ability of the models explaining the variation of course creation due to variation of independent variables is very high. The F-value shows that the explanatory variables are jointly statistically significant in the model and the Durbin-Watson (DW) statistics reveals that there is autocorrelation in the models.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".