WhatsApp Messaging: Achievements and Success in Academia
Bibliographic record
Abstract
In recent years, there has been a significant rise in the use of technological means in general and in academic teaching in particular. Many programs have been developed that include computer-assisted teaching, as well as online courses at educational institutions. The current study focuses on WhatsApp messaging and its use in academia. Studies have found that class WhatsApp groups serve for communicating with students, nurturing a social atmosphere in the classroom, forming dialogue and collaborations between students, and as a means of learning. The current study explored students' level of achievements and satisfaction as part of a WhatsApp group in a case study of a seminar course, with the aim of investigating whether use of a WhatsApp group as part of guiding an academic seminar will improve achievements in writing the seminar paper. The findings show a significant positive relationship between the achievements of WhatsApp users and their satisfaction, such that the higher the achievements of WhatsApp users the higher their satisfaction. This tool was found to have a strong effect on students' achievements. The current findings illuminate the possibilities offered by technological tools for teaching practice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".