Supporting Alignments in Scientific Activity: Moving Across Question, Evidence, and Explanation
Bibliographic record
Abstract
A core practice of science is planning and conducting investigations.This practice needs reconceptualizing, to account for where work happens between identifying a phenomenon and designing an investigation, and between gathering and analyzing data to support developing an explanation of that phenomenon (Manz et al., 2020).Teachers, supported by curriculum materials, need to engage students in becoming more involved in the decisions related to what data to choose as evidence, how to represent data to answer specific questions, and what conclusions can be drawn from data.We present results of a design study in which students investigated a dataset to answer a question about a major change to an ecosystem, using a technology tool, CODAP.We explore how the curriculum and teacher supported students in taking up different facets of data practices that support figuring out a phenomenon while moving between investigating and developing explanatory models.
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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 | 0.399 | 0.499 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.013 | 0.079 |
| Scholarly communication | 0.039 | 0.075 |
| Open science | 0.015 | 0.040 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".