NiCE Teacher Workshop: Engaging K-12 Teachers in the Development of Curricular Materials That Utilize Complex Networks Concepts
Bibliographic record
Abstract
Our educational systems must prepare students for an increasingly complex and interconnected future, but teachers facing this task are not equipped to prepare students to succeed. Network science–the study of how biological, social, physical and technological systems interconnect, how the structure of those connections evolve over time, and how those structures and behaviors inform our understanding of them–is a pathway to deepening engagement with the kinds of complex problems these students will have to deal with as adults in the workforce. We recently held the Networks in Classroom Education (NiCE) workshop for a group of 21 K-12 teachers with various disciplinary backgrounds. The explicit aim of the workshop was to introduce them to concepts in network science, show them how these concepts can be utilized in the classroom, and empower them to develop resources using these concepts, in the form of lesson plans, for themselves and for the wider community. Here we detail the nature of the workshop and present its outcomes, including a set of publicly available innovative lesson plans. We also discuss the future development of the successful integration of network science in K-12 education and its importance in inspiring and enabling our teachers.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".