Reasoning, granularity, and comparisons in students’ arguments on two organic chemistry items
Bibliographic record
Abstract
<div><div><p>In a world facing complex global challenges, citizens around the world need to be able to engage in scientific reasoning and argumentation supported by evidence. Chemistry educators can support students in developing these skills by providing opportunities to justify how and why phenomena occur, including on assessments. However, little is known about how students’ arguments vary in different content areas and how their arguments might change between tasks. In this work, we investigated the reasoning, granularity, and comparisons made in students’ arguments in organic chemistry exam questions. The first question asked them to decide and justify which of three bases could drive an acid–base equilibrium to products (Q1, <i>N </i>= 170). The majority of arguments exhibited relational reasoning, relied on phenomenological concepts, and explicitly compared between possible claims. We then compared the arguments from Q1 with arguments from a second question on the same final exam: deciding and justifying which of two reaction mechanisms was more plausible (Q2, <i>N</i> = 159). The arguments in the two questions differed in terms of their reasoning, granularity, and comparisons. We discuss how course expectations related to the two questions may have contributed to these differences, as well as how educators might use these findings to further support students’ argumentation skill development in their courses.</p></div><p></p></div><p></p>
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".