A UTAUT Evaluation of WhatsApp as a Tool for Lecture Delivery During the COVID-19 Lockdown at a Zimbabwean University
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
Focusing through the lens of the (COVID-19) lockdown which was enforced on the 30th of March 2020, it became apparent that students from rural resource-constrained educational institutions had to adapt to sustainable online learning platforms from traditional content delivery. WhatsApp a social networking app, but due to its low data consumption, it became a de-facto teaching and learning tool for Lupane State University (LSU) students in Zimbabwe. Prior studies have focused on the use of WhatsApp as an alternative lecture delivery platform but very few have evaluated its role as the sole platform for lecture delivery. With no government or institutional support for data acquisition, students failed to utilise other e-learning platforms that were in place due to exorbitant data costs. This study seeks to evaluate the success of WhatsApp mediated teaching and learning at LSU during the COVID-19 pandemic. This was a randomized evaluation of weekly lecture delivery through WhatsApp to LSU students. A questionnaire based on the Unified Theory of Acceptance and Use of Technology’s main constructs was delivered to 200 students that were randomly selected. The results revealed that student’s attitudes, behavioral intention of using WhatsApp for learning as well as the platform’s usefulness were rated highly, implying high adoption. The positive perceptions suggest that it would be easy for the institution to formally integrate the platform to augment traditional lecture delivery or for use during an event that disrupts traditional face-to-face lecture delivery. Results revealed that WhatsApp can support 21st century learning through autonomous, collaborative and learner centred education.
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 | 0.001 |
| Open science | 0.001 | 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