Interaction and Communication: An Examination of Gender Differences in Elementary Student Mathematics and Science Learning Using CMC
Bibliographic record
Abstract
In this study, gender difference is explored from two perspectives: 1) student interaction patterns, and 2) communication patterns. The data used is collected from a fifth- and sixth- grade classroom in an inner city elementary school in Toronto, Ontario. There were 24 students (12 male students and 12 female students) in the class. First, the interaction patterns of students' mathematics and science learning were examined in terms of turn taking, conversation initiating, and conversation following. The results of the analysis show that male students still take more turns in this CMC setting. Male and female students are equally likely to initialize topics. Those male generated messages were significantly less likely to be followed than those female generated messages. But male and female students are just as likely to follow and support previous messages in this CMC setting. Based on these results, gender differences are then examined with respect to student communication pattern. Communication is explored in terms of language functions. The analysis of the data indicates that female students tend to request more information, but offer fewer explanations and opinions than male students do. With respect to connected initiating messages, female students are found to be similar to male students in the use of the five language functions. However, moving to conversation development, two significant gender differences are found in student use of language functions: female students tend to request more information but offer fewer explanations than male students do in those followed-up messages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".