Instructional and Business Continuity Amid and Beyond COVID-19 Outbreak: A Case Study from the Higher Colleges of Technology
Bibliographic record
Abstract
With the announcement of COVID-19 as pandemic, and the call for social distancing, academic institutions reacted by shutting campuses and calling for the shift to virtual online delivery. In HCT, we conducted this study in an effort to sustain success in these global challenging times of crisis that is informed by strategic foresight.HCT called for an all-online delivery starting March 22, 2020 after a two-day pilot in the preceding week. HCT readiness is a result of orchestrating: an ecosystem perspective on digital transformation, a forth-looking institutional strategy that has technology utilization as a major pillar, an education technology strategy, and a comprehensive set of intelligent learning tools.Forward-looking scenarios were designed based on two critical uncertainties: (1) COVID-19 longevity and (2) socio-economic disruption. These scenarios are: Divine Mercy, Recovery Mode, New Norm, and Survival of the Fittest. Subsequently, the features of each scenario are assessed for implications on HCT’s business and support operations, and the proactive strategies are documented to cope with these implications.During the full online delivery mode period, HCT recorded 86% satisfaction amongst its faculty and 54% amongst its student body, delivered 234,000 hours through 61,000 online classes, and delivered over 21,000 hours of online professional development (PD). Over the same period, more than 1900 non-faculty employees have been running business as usual from home.Envisioning future scenarios and preparing the organization for them is a practice that should be deployed in parallel to emergency response efforts to ensure successful business continuity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".