Synchronous and Asynchronous Teaching: Realigning Teaching Approaches to a VUCA World
Bibliographic record
Abstract
The pandemic brought many challenges, stress and difficulties and, for many individuals and institutions, an unprecedented uncertainty levels. Many countries are still moving from relaxing to tightening lock-downs. While causing major disruptions, VUCA situations provide important and acute lessons. Our symposium brings together an international group of authors whose four papers (spanning quantitative, qualitative and case study methodologies) that investigated how students, academics and business schools responded to the challenges brought by the pandemic. The four papers discuss complimentary issues: Swiss school faculty emotional reactions and gaining resilience; the challenges that lecturers in Canadian business schools using Experiential Learning faced when attempting to translate those methods to the virtual classroom; how lecturers used a Transformative Learning Pedagogy approach to design a doctoral research methods course delivered online in the US; and an analysis of several ethical dilemmas that faced a leading Indian business school and how they were met. The insights that these scholars bring are relevant not only for the changes in teaching modes brought about by the pandemic but for a host of unexpected situations faced in an increasingly VUCA world. Exploring the Lived Experiences of Faculty Negotiating the Challenges of Remote and HyFlex Delivery Presenter: Ruth Puhr; Les Roches Global Hospitality Education Presenter: Rachel Germanier; Les Roches Global Hospitality Education The Experience of Integrating Experiential Learning into Online Courses amid the Covid-19 Pandemic Presenter: Melanie Ann Robinson; HEC Montreal Presenter: Marine Agogue; HEC Montreal Presenter: John Edward Fiset; Memorial U. of Newfoundland Presenter: Chantale Mailhot; HEC Montreal On Becoming a Qualitative Researcher: Transformative Learning Pedagogy in an Online Research Course Presenter: Kathy Dee Geller; Fielding Graduate U. Presenter: Kristine Lewis Grant; Drexel U. Presenter: Joy C. Phillips; Drexel U. Moral and Ethical Dilemmas during the Rapid Transition to Synchronous Online Teaching Presenter: Vasanthi Srinivasan; Indian Institute of Management, Bangalore Presenter: Sourav Mukherji; Indian Institute of Management, Bangalore
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".