The Increasing Role of Technology in Teaching and Learning Activities in Higher Education
Bibliographic record
Abstract
We are pleased to publish the second regular issue (Volume 13, Issue 2) of Higher Learning Research Communications (HLRC) for 2023. If there is a common theme that emerged from the COVID-19 pandemic, it is the increased role that technology did and will continue to play in teaching and learning activities in tertiary education. The range of articles reflects the interest in digital teaching and learning and includes the use of scaffolded simulations, the influence of immersive virtual reality in the classroom, and gamification. In addition, guidelines around instant messaging are proposed that should continue the conversation around the ethical use of technology in teaching and learning. As is typical in the HLRC, the authors reflect diverse countries, including Canada, India, Malaysia, Mexico, South Africa, and the United States. We look forward to 2024, when we expect to publish a special issue on English language influence in higher education teaching, learning, and research.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".