Giving Voice to Team Members: IM and Texting Conversation Networks in Classrooms
Bibliographic record
Abstract
We investigated the effectiveness of text messaging to facilitate discussion and learning in business and management courses. The design has three groups, face-to-face (FTF), only Instant Messenger (IM) and only cell phone text messaging (TXT - texting or SMS). All participants took a pre-test to ascertain baseline subject matter knowledge, experience with the technologies and perceptions. Over a five-day period, participants attended a lecture in a classroom on the subject matter, and follow the lecture by deliberation and discussion with their respective teams. In addition to collecting pre and post lecture survey data, we collected network data in order to identify who provide and gained the most knowledge among team members. We also generated group member level networks based on IM and texting frequencies and code category networks based on the content of these FTF, IM and TXT conversations. All eight rounds of data collection have been completed and this paper presents results from the IM and text network analysis and code category network analysis. The results of these network analyses provide insights into peer learning based on small group network relationships and the presence of the Most Knowledgeable Other (Vygotsky, 1978) as an additional factor motivating knowledge transfer and learning in small groups by giving group members a voice in these mediated conversations. The code category (functional-thematic code categories) networks also indicate the convergence in these conversations. The findings from this research can be used to explore an additional dimension of learning in school and university classrooms.
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 | 0.006 | 0.031 |
| 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 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".