Development and validation of the moon phases concept inventory for middle school
Bibliographic record
Abstract
We present the development and validation of a new assessment tool, the Moon Phases Concept Inventory for Middle School (MPCI-MS), a concept inventory about the phases of the moon targeting students aged 10 to 14 years old. Items in the questionnaire are based on a careful examination of the concept domain of phases of the moon, ideas and concepts necessary to understand the mechanism of lunar phases, as chosen by a panel of seven professional astronomers. Questions and multiple-choice answers were tested for readability with 5th grade students, tested for reading level, and submitted to a second panel of professional astronomers to check for face and construct validity of the items. The MPCI-MS was tested with N 296 students from grade 5 in elementary school to secondary 2 (M age 10.2 to 14.1). One item about global perspective on lunar phases had to be removed because of poor psychometric properties. The revised MPCI-MS has a post-test Cronbach alpha score of 0.786 and good overall psychometric properties: the mean difficulty index for the MPCI-MS pretest is 0.47, and 0.61 for the post-test; mean point-biserial correlation (post-test) is 0.376. Test-retest without instruction at one-week interval showed high test-retest reliability [M pre 13.696, M post 14.523; t45 1.315, p 0.192]. We conclude that the MPCI-MS is a reliable and valid instrument that can discriminate between novices and experts, and can be used to assess 10 to 14 year-old students' learning gains on the topic of lunar phases. The final version of MPCI-MS is a 19-item instrument, including two new questions about eclipses, that takes between 15 and 25 min for students to complete.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".