Academic journal article The American Biology Teacher

The Virtual Genetics Lab: A Freely-Available Open-Source Genetics Simulation

Academic journal article The American Biology Teacher

The Virtual Genetics Lab: A Freely-Available Open-Source Genetics Simulation

Article excerpt

We have developed the Virtual Genetics Lab (VGL), a computer simulation of transmission genetics in a hypothetical insect. The program presents a student with a genetic phenomenon-the inheritance of a randomly-selected trait. The student's task is to determine how this trait is inherited. He/she constructs a genetic model for the phenomenon by designing his/her own experimental crosses; the results of these crosses are generated by the software. He/she then analyzes the results, revises hypotheses as necessary, and continues investigations until convinced that he/she has determined how the trait is inherited. The open-ended nature of the simulation supports a variety of strategies for genetic analysis. We have found the software to be a useful way to reinforce concepts of genetics as well as to illustrate how scientists test hypotheses. We have used VGL successfully at the introductory undergraduate level; similar software has been used successfully in high schools (Slack & Stewart, 1990) and middle schools (Echevarria, 2003).


Genetics is a core element of modern biology and a part of many high school and college biology classes. In many of these courses, students show that they understand the principles of genetics by being able to solve genetics problems. Because problem solving can often require a deep understanding of the material, it is used as a tool for both teaching and evaluation. Many genetics problems require cause-to-effect reasoning (for example, "Predict the offspring from a cross of ... "); however, research has shown that some students can successfully solve these problems even when their understanding of fundamental genetic concepts is weak (Stewart, 1983). In contrast, the most challenging problems require effects-to-cause reasoning (for example, "Given these data, how is this trait inherited ... "). Because these problems require a deeper conceptual understanding, they will be more effective tools for learning

Hypothesis generation and testing are central to the scientific process; thus learning to test hypotheses in a scientific manner is also a common goal of most beginning biology courses. Many science education reform documents call for science students to learn this skill; for example, the NRC's National Science Education Standards (NRC, 1996) call for students to know how to "Design and conduct scientific investigations," and (Bransford et al., 1999) strongly encourage science classroom activities where " ... students design studies, collect information, analyze data, construct evidence, and then debate the conclusions that they derive from their evidence" (p. 171). However, it is often difficult to provide challenging hypothesis-testing activities given the limited class time, student experience, and scientific equipment found in many teaching laboratories.

Several computer simulations of genetics have been developed that offer students a chance to practice effects-to-cause reasoning and hypothesis testing. Each application allows the student to design crosses; the program calculates the results, which the student must then interpret. CATLAB ( html) is a simulation of the genetics of coat color, tail length, and other traits in cats; use of CATLAB has been shown to require an in-depth understanding of genetics (Simmons & Lunetta, 1993). Biologica (www.concord. org/biologica) is a collection of genetics simulations involving pea plants and dragons; it too has been shown to be an effective tool for teaching genetics (Hickey et al., 2003). The Virtual Flylab ( is a simulation of fruit fly genetics that is used in many classrooms.

The Genetics Construction Kit (GCK, www.bioquest. org/indexlib.html; Soderberg et al., 1994) simulates fruit fly genetics. Studies of GCK have shown that students must use their knowledge of genetics to solve problems with this software (Hafner & Stewart, 1995; Finkel, 1996). …

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