Science teacher's assessment of their students’ models
Bibliographic record
Abstract
This study explores science teachers’ assessment of their students’ models. The Questionnaire of Assessment Literacy (QALMBT-Modeling) was developed to investigate the range of in-service science teachers (ISTs)’ assessment practices in model-based teaching (MBT). The questionnaire was informed by an assessment literacy (AL) framework and rooted in studies on modelling in science education. This Likert-type scale questionnaire was administered to 386 middle and high school science teachers in Chile. Statistical analyses demonstrated strong reliability and structural validity of the scale and exploratory factor analysis revealed three key dimensions of AL in MBT (ALMBT): (1) assessment strategies to elicit, evaluate, and support student modelling; (2) facilitation of student-led evaluation and refinement of models through peer and self-assessment, (3) and communication of assessment criteria for student models. Analysis of item-level data showed that while teachers frequently provide feedback and evaluate students’ conceptual understanding of models, they less often involve students in peer/self-assessment or use student-generated criteria to assess models which are practices critical to epistemic growth in MBT. This work supports future efforts to refine assessment practices and promote reflective, model-rich science instruction, particularly during the evaluation and modification phases of the instructional cycle.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".