Enhance, Extend, Empower: Understanding Faculty Use of E-Learning Technologies
Bibliographic record
Abstract
There has been scant nation-wide assessment of institutional use of learning technology in Canada (Grant, 2016) and where assessment has been done of student access to e-resources, considerable variability within and across institutions has been reported (Kaznowska, Rogers, & Usher, 2011). With a broad goal of improved and increased use of learning technologies, one university wanted to explore the use of e-learning technologies across campus. The purpose of this study was to identify instructors' needs and aspirations with respect to how learning technologies at the university could be designed, implemented, and supported. The 3E framework of Enhance, Extend, Empower, proposed by Smyth, Burce, Fotheringham, & Mainka (2011), was useful in examining the underlying purposes of using e-learning technologies. For this qualitative study, the research team engaged 32 instructors in individual interviews or in focus groups to discuss how they currently use e-learning technologies, how they hope to advance their uses of these technologies, and their perceived barriers or enablers to implementation. The study has implications for practice and policy at postsecondary institutions; additionally, this study suggests possibilities for further research into the scholarship of teaching and learning in the context of e-learning technologies.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".