NAIT eLearning Initiative: Building an Enabling Envioronment- Five Years Later: Lessons Learned.
Bibliographic record
Abstract
Five years later, how did the Northern Alberta Institute of Technology (NAIT) manage a large-scale integration and adoption of a fully online and blended learning model of instructional delivery? The organization began with a pilot project in 2006 to gather experience and knowledge that would ensure our institute-wide NAIT e Learning Initiative was successful. The journey took our organization forward to full development of online degrees in less than one year. What NAIT experienced, the cost, what worked and what didn‟t work, the unprecedented growth in online learning and other lessons learned over the past five years will be discussed. Key strategies used throughout the process such as relying closely on NAIT faculty support, training and involvement; paralleling Alberta‟s other postsecondary organizations goals, NAIT moved to a more „organized‟ and institution-wide approach to the development, delivery, and support of online learning. Articulating NAIT‟s learning philosophy, created a unique e learning instructional design matrix (and course development template) that; defined and refined our learning management systems policies and practices for course delivery and student feedback and support were used to realize a positive outcome of our strategy. Continually increasing the skill sets of NAIT faculty in using the technology students have come to expect became imperative and also contributed to our success. Finally, incorporating best practice, being flexible and providing online training and support to faculty who have come to understand how teaching online is different from face-to-face classroom instruction
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".