"Texting to Overcome Language Barriers, Collaborate and Facilitate Knowledge Transfer"
Bibliographic record
Abstract
Texting by students in classrooms has become so prevalent, that it warrants an investigation into possible uses of this medium for engagement with students and tacit and explicit knowledge transfer to and among students. We describe a mixed-methods study on the use of texting to facilitate classroom discussions and student engagement. The study involved teaching of intensive 5-day courses in management subjects with participants who were divided into three medium-based discussion groups - face-to-face (FTF), instant messenger (IM) and texting (TXT). During the course of the study, participants discussed topics on the subject matter using the medium assigned to their group. Participants also completed surveys on technology efficacy, motivation to participate in the study, communication preferences, social and group dynamics issues and learning outcomes. Results from the confirmatory factor analysis of hypotheses indicate some support for using texting to discuss course material, and strong support to enhance communication between teammates, to overcome language barriers, and to focus on task. The findings from this research reveal an additional dimension of learning in school and university classrooms that is of particular interest in facilitating classroom engagement across diverse student populations.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".