Two-tier Multiple-choice Questionnaires to Detect the Students’ Misconceptions about Heat and Temperature
Bibliographic record
Abstract
This study aimed to develop a two-tiers diagnostic test to assess the high school, junior high school, and elementary pre-service teachers about the heat and the temperature concepts in a general physics course. There are two tiers in this test: The first tier composed of six items consisting of multiple-choice questions related to the heat and the temperature, including the correct answer. The second tier of each item contains reasons for students choosing their answer to the first tier. The second tier included four or five responses, one of which is a correct conceptual understanding. The wrong answers, also called distractors, were based on students’ misconceptions. To this end, 128 pre-service teachers from Quebec in Canada completed a pencil-paper questionnaire of sixty minutes duration composing of six questions (four open-ended questions and two multiple choice questions with justifications). As illustrations, the following conceptual understandings have been identified in our qualitative analysis of the data collected: 1. The change of state of the matter does not require a constant temperature; 2. The temperature is a measure in degrees to indicate the level of heat of an object or person; 3. The mercury contained in a thermometer expands when it is heated so that the particles which constitute it expand; and 4. The sensation of cold (or warm) is related to the difference in temperature.
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 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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".