Social Network Analysis to Examine Interaction Patterns in Knowledge Building Communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes use of social network analysis to examine student interaction patterns in a Grade 5/6 Knowledge Building class. The analysis included face-to-face interactions and interactions in the Knowledge Forum® Knowledge Building environment. It is argued that sociogram data are useful to reveal group processes; in sociological terms, the community lies in the connections among the group. A classroom of unconnected individuals is unlikely to form as a Knowledge Building community; data analyses reported in this study show promise in understanding the dynamics of Knowledge Building in a consistent and measurable way. The strength of the work is not in particular patterns demonstrated but in new forms of assessment and their potential to inform work as it proceeds. The research reported shows that teachers and students are finding social network analysis useful and that through their engagement research-practitioner-engineer teams are better positioned to develop tools to advance Knowledge Building pedagogy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it