GenBio-MAPS: A Programmatic Assessment to Measure Student Understanding of <i>Vision and Change</i> Core Concepts across General Biology Programs
Bibliographic record
Abstract
The Vision and Change report provides a nationally agreed upon framework of core concepts that undergraduate biology students should master by graduation. While identifying these concepts was an important first step, departments also need ways to measure the extent to which students understand these concepts. Here, we present the General Biology-Measuring Achievement and Progression in Science (GenBio-MAPS) assessment as a tool to measure student understanding of the core concepts at key time points in a biology degree program. Data from more than 5000 students at 20 institutions reveal that this instrument distinguishes students at different stages of the curriculum, with an upward trend of increased performance at later time points. Despite this trend, we identify several concepts that advanced students find challenging. Linear mixed-effects models reveal that gender, race/ethnicity, English-language status, and first-generation status predict overall performance and that different institutions show distinct performance profiles across time points. GenBio-MAPS represents the first programmatic assessment for general biology programs that spans the breadth of biology and aligns with the Vision and Change core concepts. This instrument provides a needed tool to help departments monitor student learning and guide curricular transformation centered on the teaching of core concepts.
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.
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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".