The Inherent Tensions of “Instant Education”: A Critical Review of Mobile Instant Messaging
Bibliographic record
Abstract
This paper critically reviews literature on the role of Mobile Instant Messaging (MIM) applications, such as WhatsApp, in supporting learning and teaching practice. Using formal qualitative synthesis as its methodology, and dialectical theory as an analytical framework, our main objective was to identify tensions, affordances, constraints, and resolution strategies in educational uses of MIM. In contrast to prior work, the analysis offers a nuanced and complex picture of the use of MIM in learning and teaching settings. Instead of facilitating the creation of educational outcomes in a straightforward manner, the realities of MIM use are socially constructed and the subject of conflictual negotiations. The educational use of MIM requires users to navigate the interdependent dialectical tensions of immediacy versus delays (temporal dimension), intimacy versus detachment (relationship dimension) and task versus ludic orientation (intellectual dimension). The findings also reveal a number of behavioural and technical resolution strategies that users deploy to manage these tensions.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".