Phys-MAPS: a programmatic physiology assessment for introductory and advanced undergraduates
Why this work is in the frame
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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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it