Student Experiences of Emergency Remote Learning and Teaching During COVID-19
Bibliographic record
Abstract
This study aimed to explore and understand the University of Cape Town student perceptions and lived experiences of Emergency Remote Teaching and Learning (ERTL) during COVID-19. COVID-19 is a communicable disease instigated by a novel virus (SARS CoV-2 virus). After the inevitable subsequent national lockdown of South Africa, the university placed ERTL measures in place for the second quarter of the first semester to curb the impact of the virus on its students while also enabling learning and teaching activities to continue remotely. ERTL meant that learning and teaching activities were ‘rapidly' shifted from face-to-face learning to remote learning. This study reports on the 707 students who responded to an online survey while engaged in their online courses. The Substitution, Augmentation, Modification, and Redefinition (SAMR) and Andersons' Online Learning Model were used to engage with students on the use of technology that enabled their interaction with lecturers, each other, learning and teaching activities, and other remote learning resources. Understanding the student experiences was achieved through a mixed-method study approach that involved undergraduate and postgraduate students. The Google form online surveys, with both open and closed ended questions with some using the 5-point Likert scale ratings, were distributed using social media platforms and university email system to students in order to collect the data. MAXQDA and Excel software were later utilised to analyse and code the data. Findings for this study indicate that the ERTL experience of the participants during the COVID-19 pandemic presented both opportunities and barriers. Some of the perceived opportunities by students were flexibility and convenience, pedagogical improvements, time saving, self-directed learning (working anytime they want and creating and managing their working schedule), and spending time with family. Interestingly enough, some of these benefits turned out to be challenges for some of the students. Hence, some of the barriers students perceived were distractions, internet connectivity and technical issues, inequitable living and environment conditions, lack of hands-on experience and how this made their degree feel incomplete and difficult, mental health issues, and many other barriers. The disciplinary faculties that experienced most of the obstacles and difficulties associated with ERTL were those whose academic experience depended on practical work in labs and studios or needed software that can only be accessed through labs and would need a specific operating system. The carrying out of this research will help ensure the effectiveness, investment, and continual integration of technology in future programs that involve learning and teaching.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".