Reasoning, Granularity, and Comparisons: A Unit-Based Method for Characterizing Students’ Arguments on Chemistry Assessments
Bibliographic record
Abstract
In a world facing complex global challenges, citizens around the world need to be able to engage in argumentation supported by scientific evidence and reasoning. In order for coming generations to have proficiency in this skill, students must be provided opportunities to develop and demonstrate argumentation science classrooms, including on assessments. For example, students can be provided with assessment items that explicitly ask them to reason from evidence. Alongside these assessment items, researchers and educators need methods to evaluate students’ written arguments. In this study, we present a unit-based method for characterising students’ arguments on chemistry assessments. This unit-based method identifies units (links, concepts, comparisons) within one’s argument, and uses these units to evaluate an argument based on three dimensions: reasoning, granularity, and comparisons. To demonstrate this method, we report our findings from using it evaluate two different organic chemistry questions: (1) justifying why one of three bases would drive an equilibrium towards products ( N = 170), and (2) justifying why one of two reaction mechanisms is more plausible ( N = 122). Lastly, to translate the method into a rubric for educators, we compare a scoring system based on the unit-based method against a traditional scoring system. As well, we report our findings from interviews with educators ( N = 4) to invite their feedback on the rubric and its dimensions.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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 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".