South African Secondary School Discussions on Digital Learning and Pandemic Preparedness
Bibliographic record
Abstract
The outbreak of the COVID-19 pandemic in 2020 revolutionised the education sector across the world and forced schools to embrace online learning. Schools had to scramble for alternatives to face-to-face learning to curb the spread of COVID-19 while ensuring that learning was not disrupted. With the second wave of the COVID-19 pandemic cropping up at the beginning of the 2021 academic year and a growing number of teachers contracting the virus, schools were forced to close temporarily or adjust learning models to continue with remote teaching and learning. This required schools to deal with the challenges of infrastructure and a shortage of teachers, as well as provide learners with access to technology and reliable internet connections that would allow them to study remotely and prepare teachers for online pedagogies. To this end, this study explored secondary teachers’ experiences with the transition to remote learning during the COVID-19 pandemic lockdown and their readiness to embrace online learning as the second wave of the COVID-19 pandemic wreaked havoc on the entire globe. The study was underpinned by the technology acceptance model and adopted a qualitative research design, generating data from 10 teachers using focus group discussions. An inductive thematic framework was used during the data analysis segment. The study found that schools encountered a variety of digital complexities to overcome, such as digital literacy and online teaching capabilities, multimodal learning, postlockdown teaching and educational leadership and appropriate learning management systems.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 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".