Bibliographic record
Abstract
Exploring students’ habits of using WhatsApp is important: such introspection helps students to reflect, and improve their actions. Habits are subconscious thoughts that drive students, for example, to use WhatsApp, even without concentrating on their learning actions. Habits are formed after students have repeated the same action. Twelve students, registered for a Bachelor of Education degree in Mathematics Education at a university in South Africa, were selected to participate. The objective of this study was to understand students’ habits of using WhatsApp in the learning of mathematics. Reflective activities, focus-group discussion, and one-on-one semi-structured interviews, framed by interpretive case study, were used for data production. The students’ habits revealed that The Tree Three Rings Theory was useful when applied as the learning framework. Such application of the theory generated three categories of habits of WhatsApp usage. Categories were social, discipline/disciplinary, and personal habits. The university at which these students were registered prescribed Moodle as the learning management system (LMS). However, the students mostly used WhatsApp. As a result, they used WhatsApp even during face-to-face classes. It appeared that students had been ‘captured’ by WhatsApp, using it as their ‘master’, instead of using it as both their ‘master’ and ‘servant’. The study concluded that, although there were elements of both personal and discipline habits, the social habits drove the learning. Consequently, this study recommends that students should reflect; using social, discipline, and personal actions as taxonomies of education habits, in order to address societal, individual, and the mathematics education needs.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".