Exploring Secondary Students’ Conceptions about Fire Using a Two-Tier, True/False, Easy-to-Use Diagnostic Test
Bibliographic record
Abstract
This article describes the design of a misconception diagnostic test about fire-related phenomena. It proposes a new test format in which a certainty-measuring tier has been integrated into each of the true/false response choices. This format is argued to be easier for teachers to use than the increasingly popular three-tier format. First, we review the available literature about misconception diagnostic tests and then literature about fire-related misconceptions. We then describe the design process of the test, which was preceded by an interview phase. We then describe its administration to 221 secondary school students. We finally present, in an explorative and accessible manner, the results that were obtained. These results support the existence of previously recorded misconceptions, but also bring certain nuances to some of their previous interpretations. They also support the hypothesis according to which some misconceptions are presumed to be more widespread than they truly are. Conclusive remarks are formulated about the benefits of the use of our—and other— misconception diagnostic tests.
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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 | 0.046 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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 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".