South African Secondary School Discussions on Digital Learning and Pandemic Preparedness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it