Phys-MAPS: a programmatic physiology assessment for introductory and advanced undergraduates
Bibliographic record
Abstract
We describe the development of a new, freely available, online, programmatic-level assessment tool, Measuring Achievement and Progress in Science in Physiology, or Phys-MAPS ( http://cperl.lassp.cornell.edu/bio-maps ). Aligned with the conceptual frameworks of Core Principles of Physiology, and Vision and Change Core Concepts, Phys-MAPS can be used to evaluate student learning of core physiology concepts at multiple time points in an undergraduate physiology program, providing a valuable longitudinal tool to gain insight into student thinking and aid in the data-driven reform of physiology curricula. Phys-MAPS questions have a modified multiple true/false design and were developed using an iterative process, including student interviews and physiology expert review to verify scientific accuracy, appropriateness for physiology majors, and clarity. The final version of Phys-MAPS was tested with 2,600 students across 13 universities, has evidence of reliability, and has no significant statement biases. Over 90% of the physiology experts surveyed agreed that each Phys-MAPS statement was scientifically accurate and relevant to a physiology major. When testing each statement for bias, differential item functioning analysis demonstrated only a small effect size (<0.008) of any tested demographic variable. Regarding student performance, Phys-MAPS can also distinguish between lower and upper division students, both across different institutions (average overall scores increase with each level of class standing; two-way ANOVA, P < 0.001) and within each of three sample institutions (each ANOVA, P ≤ 0.001). Furthermore, at the level of individual concepts, only evolution and homeostasis do not demonstrate the typical increase across class standing, suggesting these concepts likely present consistent conceptual challenges for physiology students.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".