Navigating Digital Environments One Step at a Time During COVID-19
Bibliographic record
Abstract
COVID-19 pandemic turned the world upside down in many ways and forced universities worldwide to reconsider established practices. As an Asia-Pacific institution with research, learning and teaching activities across two countries, James Cook University (JCU) Singapore was no stranger to digital learning and had well-established, blended-learning policies (JCU, Blended learning design cycle, https://www.jcu.edu.au/__data/assets/pdf_file/0004/227866/JCU-Blended-Learning-Guide-2015.pdf , 2014) in place, based on constructive alignment of learning objectives, teaching methods, and assessment. This retrospective chapter examines the impact of the pandemic on learning and teaching and discusses how the experience shaped student-centric digital environment where students are actively engaged in reflecting on their learning as well as in the construction of knowledge—the impact on academic culture, social activities, collaborative efforts, and professional learning, both in and out of the classroom. Our considerations for quality digital learning design are informed by collaborative constructivism. and collective learning. From the institution’s reflective approach to crisis management emerged new processes and standards for digital learning that present opportunities for holistic digital transformation in Higher Education (HE) to policy makers and faculty.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".