Interaction and Communication: Elementary Students' Learning of Mathematics and Science in a CMC Setting
Bibliographic record
Abstract
This study was intended to contribute to knowledge in the area of collaboration in the context of computer-mediated communication (CMC). In this study, collaboration was explored in terms of students' interaction in their learning process. The primary purpose of this study was to examine relationships between language functions associated with the messages generated in the context of CMC and participants' interaction. The secondary purpose to explore the use of language functions in relation to teacher-student interactions. Results of this study indicated that participants (including teachers. students, researchers, and scientists) were actively participating in collaboration in the context of CMC. Out of five language functions examined, two language functions used by participants in the context of CMC showed significant relationships with interaction. Participants' use of “giving explanation” and “expressing disbelief” was positively associated with their interaction. One interesting finding of teacher-student interaction was that with teacher and helping adult involvement, participants were more likely than students alone to make suggestions. When scrutinizing the pattern of the use of language functions in first messages, it appeared that the students' strategy was to ask a lot of information, while with teachers and helping adults engagement, participants used “presenting opinion” most frequently. Educational implication of this study was discussed. In addition, recommendations for future research about collaboration in the context of CMC were made.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".