A UTAUT Evaluation of WhatsApp as a Tool for Lecture Delivery During the COVID-19 Lockdown at a Zimbabwean University
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.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".