Empowering twenty-first century assessment practices: designing technologies as agents of change
Bibliographic record
Abstract
The overarching questions guiding this interprofessional design-based research study are: (1) How might a suite of assessment tools help K-7 educators visualize learning in their classrooms and (2) How might these visualization approaches inform K-7 educators’ changes in classroom assessment? Recognized by their administrators as having previously introduced twenty-first century learning and teaching into their classrooms, seven primary educators (grades K-3) and two intermediate educators (grades 4-7) volunteered to participate in this study. Across three data collections, researchers explored how these K-7 educators perceived an impact to their classroom practices when introduced to a new suite of assessment tools. All K-7 educators reported the importance and challenges of visualizing and capturing individual, small group, or whole-class formative learning artifacts in their classrooms. They reported the following characteristics were important: interactive, personalized, collaborative, creative, and innovative. Reflecting on their brief experiences with the software, the K-7 educators reported more confidence in using the suite of assessment tools. They appreciated working as part of an interprofessional team including researchers, academics, and software developers. Based on these initial findings, the researchers discuss the study’s scholarly significance, position the study within the growing literature, and suggest such opportunities may initiate just-in-the-moment professional development.
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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 | 0.046 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".