The Use of Strategic Simulations
fern The software in The BioQUEST Library exemplifies what we call "Strategic Simulations." We feel strategic simulations take full advantage of the computer in terms of:

  1. novelty of problems each time a program is run
  2. realistic outcomes for each experiment performed
  3. infinite opportunities to perform experiments
  4. computational power
  5. speed in obtaining results
  6. large data size
  7. facilitating successive hypothesizing and logical and numerical testing
  8. sequentially developed problem difficulty involving an increased quantity of natural phenomena. (Jungck and Calley, 1985)
  9. hypothesis as solution
  10. opportunity for peer review


Students work within microworlds, where each microworld is built primarily upon an analogue model of an organism, organismal components, groups of organisms and environments as appropriate. Students can try out scientific strategies before going into the lab or extend their skills beyond that which could pragmatically occur in the lab because of temporal, fiscal, safety, ethical, technical and material considerations.

The BioQUEST Library modules and supplementary materials allow students free experimentation with apparatus and analysis tools commonly available in a well-equipped research laboratory. For example, Genetics Construction Kit (Jungck and Calley,1986) realistically simulates the phenomena of transmission genetics. We will use the example of Genetics Construction Kit (GCK) to illustrate some of the design principles common in BioQUEST simulation software.

GCK begins by providing a random "field collection" of organisms with different phenotypic traits. It provides tools for performing genetic crosses, statistical spreadsheets for analyzing the outcomes, and a lab book for recording hypotheses. Given these tools, students can ask:

  • Which trait variations do they suspect are inherited and wish to investigate?

  • How will they proceed with their investigation? What crosses will they make?

  • What hypotheses can be drawn from their data?

  • How can these hypotheses be confirmed or rejected?

  • When confirming a hypothesis, what new data would result in new conclusions?

  • When do you feel that you have accumulated enough data and have explored the problem space sufficiently to make your conclusions public?

The last question is particularly pertinent for illustrating a parallel between The BioQUEST Library simulations and real world research. The use of simulations challenges students to address the question of closure and the issue of correctness. In other words, it enables students to appreciate the variety of factors related to when and why scientists decide that research is "done." The open-ended feature of The Library's realistic laboratory simulations helps teachers confront students' obsessions with "right" answers and promotes meaningful, rather than rote, learning of science.

The open-endedness of the simulations also changes the role of faculty and students. The teacher's role moves from that of an authoritarian holder of encyclopedic knowledge to that of a coach and mentor. Students are no longer passive recipients of information; they are empowered to gain control and experience in developing integrative thinking. In addition, the negative view of errors and "wrong" answers typically held by students subsequently changes to one in which errors become a positive and powerful means to debugging complex problems. The teacher and students are able to perform scientific experiments and make quantitative analyses of experimental results, but as no "answers" are necessarily available, they experience the same sort of thinking as would a research biologist. By working in such realistic laboratory environments, teachers and students can focus on strategies and useful heuristics: What factors need to be considered when formulating and solving a research problem? Why are some questions and strategies better than others? Teachers can aid students in posing problems, examining their reasons for performing particular experiments and their predictions, making sense of the data they have collected, constructing robust tests of hypotheses, and generating alternative hypotheses. In other words, the importance is now on the process of problem-solving and not just an end point (the "answer").