Readiness of Students for Multi-Modal Emergency Remote Teaching at A Selected South African Higher Education Institution
Bibliographic record
Abstract
The closures of Higher Education Institutions (HEIs) due to the Covid-19 pandemic meant that face to face classes had to be put on hold. However, the growth in information and communication technologies (ICT) made it possible for HEIs to continue with their core activities remotely, primarily using learning management systems (LMSs). The overuse of LMS at the selected HEI resulted in the former’s collapse. The consequence was that management of the institution advised lecturers to use multi-modal emergency remote teaching (ERT) to save the academic year. Lecturers adopted a variety of platforms and approaches, largely depending on their preferences. This study identified the ICT platforms and approaches used by lecturers during remote teaching as well as estimating the readiness of students for emergency remote learning. Readiness was established with the use of the Technology Readiness Index 2.0 (TRI2.0) of the Technology Readiness Model. In addition, the effects of age, gender and level of study on technology readiness were estimated. A self-administered questionnaire was shared with senior students within the accounting department of the selected HEI. Descriptive and inferential statistics were used to analyse the data collected from 243 respondents. The study found that Microsoft teams was the commonly used platform whilst pre-recorded lectures and live classes were the popular approaches used. In terms of technology readiness, the study found that students were not ready as indicated by a low TRI 2.0 of 2.8. Age and study level had a positive effect on technology readiness. To provide the best possible learning experiences to students, lecturers need to understand what worked, what did not and why. The results of this study provide invaluable information and lay a foundation for successful future e-learning projects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".