Patterns of Scientific Reasoning Skills among Pre-Service Science Teachers: A Latent Class Analysis
Bibliographic record
Abstract
We investigated the scientific reasoning competencies of pre-service science teachers (PSTs) using a multiple-choice assessment. This assessment targeted seven reasoning skills commonly associated with scientific investigation and scientific modeling. The sample consisted of 112 PSTs enrolled in a secondary teacher education program. A latent class (LC) analysis was conducted to evaluate if there are subgroups with distinct patterns of reasoning skills. The analysis revealed two subgroups, where LC1 (73% of the PSTs) had a statistically higher probability of solving reasoning tasks than LC2. Specific patterns of reasoning emerged within each subgroup. Within LC1, tasks involving analyzing data and drawing conclusions were answered correctly more often than tasks involving formulating research questions and generating hypotheses. Related to modeling, tasks on testing models were solved more often than those requiring judgment on the purpose of models. This study illustrates the benefits of applying person-centered statistical analyses, such as LC analysis, to identify subgroups with distinct patterns of scientific reasoning skills in a larger sample. The findings also suggest that highlighting specific skills in teacher education, such as: formulating research questions, generating hypotheses, and judging the purposes of models, would better enhance the full complement of PSTs’ scientific reasoning competencies.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".