Meaningful interactions with curriculum materials: pre-service teachers speak about their experiences
Bibliographic record
Abstract
The curriculum materials center (Curriculum Lab) at our institution enjoys broad support from the School of Education that oversees it and the pre-service teachers who embrace the services and materials it provides. Despite this support, there is an ongoing need to articulate and justify the significant role the Curriculum Lab plays in the delivery of our teacher education program. A current research project aims to collect video statements of on-campus or field work related to Curriculum Lab materials and services. Participants are asked to share a specific experience and then consider its impact: “how does this experience contribute to your work and growth as a pre-service teacher/teacher educator/practicing teacher?”. Thematic analysis of the statements will provide insight into how respondents view the services and materials provided by the Curriculum Lab. Excerpts from statements will also be compiled into one video so that participants’ own words might inform the central research question. We intend to demonstrate the value of an overall strategy to connect pre-service teachers with quality teaching and learning resources, while also offering a model for the curriculum materials center as integral to program delivery.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".