Pre-Service Teachers’ Levels of Adaptations to Remote Teaching and Learning at A University in A Developing Country in the Context of COVID-19
Bibliographic record
Abstract
The outbreak of Covid-19 pandemic has not only caused fear and uncertainty in the education systems across the globe, but it brought about a fundamental paradigm shift in the mode of teaching and learning. Higher education drastically transitioned to remote/ online delivery even for the students who had enrolled for face-to-face mode of teaching and learning. The paper is premised in the context of a developing country that such a drastic change could have widened the digital divide between students from privileged homes and those from disadvantaged families as students did not receive adequate technological training and to even acquire the necessary electronic devices. Consequently, the study sought to establish the levels of adaptation to remote teaching and learning by university students herein referred to as pre-service teachers. Following a quantitative research design, an online questionnaire survey was administered to 157 pre-service teachers enrolled in a Life Sciences Methodology module at a South African university. Data was analysed using SPSS version 26 and descriptive statistics, exploratory analysis of the questionnaire constructs and One-Way ANOVA tests were conducted to compare pre-service teachers` perceptions, experiences and preparedness. The results showed that the disparities and inequalities that exist in different South African contexts in which pre-service teachers hail from, dictated their levels of adaptations to remote teaching and learning. Those from disadvantaged backgrounds were less adapted as they struggled more when it comes to acquisition of electronic gadgets and connectivity to facilitate remote learning compared to those from advantaged backgrounds. This study affirms the call for education institutions and governments to rethink ways of closing the gap between the poor and the rich in education in terms of resource and other support mechanisms.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".