Instructors' Perspectives on Learning Technologies in the Multidisciplinary Faculty of Land and Food Systems at the University of British Columbia, Canada
Bibliographic record
Abstract
The advent and rapid development of emerging technologies for teaching and learning present unprecedented opportunity for applications to higher education.Challenges arise to develop instructors' competencies and pedagogical use of learning technology to improve teaching and learning outcomes.To understand faculty members' perceptions of learning technologies and their current practices at the Faculty of Land and Food Systems, an investigation was carried out through semi-structured interviews (n=23).Instructors were aware of existing and emerging technologies but face several challenges, including: whether emerging technologies augment teaching practices to achieve the desired learning objectives, the time needed to learn, adopt and adapt the techniques, establishing priorities in an already demanding schedule, and the limitations of technical and physical resources to allow effective use of the technology.Although instructors were willing to adopt the technologies, the type of training or assistance desired was inconsistent or not clearly articulated in interviews.Participants were positive about opportunities to communicate and share experiences with fellow instructors, but that emerging technologies require training.This training should be recognized as professional development considered in employee reviews and evaluations, and incentivized by incorporating it into career enhancement programs.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.010 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".