Developing of an instrument for assessing students’ data analysis skills in the undergraduate physics laboratory
Bibliographic record
Abstract
This paper describes the development and field test of the Laboratory Data Analysis Instrument (LDAI), aimed at diagnosing first-year students’ understanding of data analysis procedures. The design of this student-centered instrument is based on difficulties related to students’ data analysis skills required in an introductory physics laboratory. Difficulties were determined from previous literature, observations in laboratory classes, and interviews with students. The LDAI is single-context based, consisting of 30 multiple-choice and true/false questions embedded in a real laboratory report from a real laboratory. It was validated by seven experts: three physics professors, one science education lecturer, and three experienced laboratory instructors. Administration to 77 participants resulted in a Cronbach’s alpha of 0.702. The LDAI is a promising step toward having a valid and reliable diagnostic instrument that can be used by laboratory instructors in undergraduate physics laboratories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".