Investigation of Three-Tier Diagnostic and Multiple Choice Tests on Chemistry Concepts with Response Change Behaviour
Bibliographic record
Abstract
This study aims to investigate the test scores of the three-tier diagnostic chemistry test (TDCT) and multiple choice chemistry test (MCCT) by response change behaviour (RCB). The study is a descriptive research study aiming to investigate the item response efforts of TDCT and MCCT in a computerized testing environment (Quizzer test program, QTP). In both TDCT and MCCT, QTP maintains a continuous record for each tier of the test. Participants in the study are students in the Science Education Department at the state university in the Aegean region of Turkey (n=115). The study was conducted in two groups: there were 58 students in Group 1 and 57 students in Group 2. In Group 1, a TDCT was used; in Group 2, an MCCT test was applied. Tests were distributed by random sampling between Group 1 and Group 2. The data were collected by adding a confirmation tier to the TDCT involving 44 items. The TDCT was applied to 115 pre-service teachers; the reliability coefficient of the test was found to be 0.72. SPSS and MS Excel programs were used to analyse the data. Data were analysed using descriptive statistical methods. Considering the results obtained from the study, the rate of completing the test with RCB of test items for both tests is approximately 7–12 per cent. Another important consequence is that RCB does not provide an advantage or disadvantage in terms of scoring.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".