Perceptions of students’ on the Use of WhatsApp in Teaching Methods of English as Second Language at the University of Namibia
Bibliographic record
Abstract
Recently, electronic mobile devices have been widely used for attaining knowledge, asking questions and retrievinginformation. Mobile devices and their features have been in the glare of publicity for educational purposes. TheWhatsApp application instant messaging platform has become the most popular mobile device application regarded asone of the teaching and learning styles that facilitate collaborative learning as students are beaming with ownsmartphones.Nowadays, it is challenging to help students raise their interest in learning. Thus WhatsApp presents itself as one of theinventive teaching methods that can attract students and provide them with opportunities for further learning.WhatsApp increases helps students to work smarter and more effectively. This research investigates the students’perceptions towards using the WhatsApp application as a learning tool for Teaching Methods of English as SecondLanguage on a Bachelor’s degree programme at the University of Namibia. To achieve this, about 99 students in thesame cohort completed the self-administered questionnaires. The study revealed, amongst many, that WhatsApp canimpact negatively on the performance of tertiary students, especially those who do not own smartphones. Theplatform shows a variance on balancing online activities (WhatsApp) and academic preparation, and distractsstudents from completing their assignments and adhering to their private studies time table. However, students enjoyusing WhatsApp as a tool for learning and calls for institutions to offer internet amenities as a top urgency incontemporary instruction.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".