Readiness of Students for Multi-Modal Emergency Remote Teaching at A Selected South African Higher Education Institution
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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