Geotechnical and geoenvironmental engineering education during the pandemic
Bibliographic record
Abstract
This paper reports the impact of coronavirus disease 2019 on the practice and delivery of geotechnical and geoenvironmental engineering (GGE) education modules, including lectures, lab sessions, student assessments and research activities, based on the feedback from faculty members in 14 countries/regions around the world. Faculty members have since adopted a series of contingent measures to enhance teaching and learning experience during the pandemic, which includes facilitating active learning, exploring new teaching content related to public health, expanding e-learning resources, implementing more engaged and student-centred assessment and delivering high-impact integrated education and research. The key challenges that faculty members are facing appear to be how to maximise the flexibility of learning and meet physical distancing requirements without compromising learning outcomes, education equity and interpersonal interactions in the traditional face-to-face teaching. Despite the challenges imposed by the pandemic, this could also be a good opportunity for faculty members obliged to lecture, to rethink and revise the existing contents and approaches of professing GGE education. Three future opportunities namely, smart learning, flipped learning and interdisciplinary education, are identified. The changes could potentially provide students with a more resilient, engaged, interactive and technology-based learning environment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".