Teaching Genetic Linkage and Recombination through Mapping with Molecular Markers
Bibliographic record
Abstract
Most introductory genetics courses cover genetic linkage, a core concept in the CourseSource genetics learning outcome framework. Although it is a classical genetics topic, genetic linkage remains an important concept to understand in order to grasp modern genetics research approaches including Single Nucleotide Polymorphism (SNP) mapping, Genome Wide Association Studies (GWAS), and gene discovery. Typically, genetic linkage is taught in a very traditional way within our introductory genetics classes. Invariably, we see students struggling with the same aspects of linkage: how to distinguish between parental and recombinant combinations of alleles and how to relate phenotype proportions to meiotic processes and outcomes. We designed a lesson that provides a practical and experimental context to target these common student difficulties in learning about linkage and recombination. This student-centered interactive lesson and associated post-class problem set teaches genetic linkage through mapping a gene by determining co-segregation of a phenotype with microsatellite sequences revealed by gel electrophoresis banding patterns. This lesson includes very interactive class sessions and a follow-up problem set and post-test that allows students to develop a deeper understanding of genetic linkage, and provides instructors with insights about student thinking. When we implemented this lesson, we observed a dramatic increase in student understanding of genetic linkage and how to use molecular markers to map the location of genes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".