Investigative Case-Based Learning (ICBL) ( http://bioquest.org/icbl/ ) is a variant of Problem Based Learning that encourages students to develop questions that can be explored further by reasonable investigative approaches. Students then gather data and information for testing their hypotheses. They produce materials which can be used to persuade others of their findings. Students employ a variety of methods and resources, including traditional laboratory and field techniques, software simulations and models, data sets, internet-based tools and information retrieval methods.

Investigative cases draw from realistic situations in which scientific reasoning can be applied. Although the case defines a general area of science under investigation, students generate specific questions to guide their study. Students investigate scientific problems that they find meaningful. In the process they also learn to:

Investigative cases are useful for lifelong learning because they are open-ended and draw from a broad range of situations in which scientific reasoning can be applied. Investigative cases necessarily shift the focus of student learning beyond the facts to include using scientific knowledge to frame questions and to answer them.

Investigative case-based learning methods incorporate problem posing, problem solving, and peer persuasion (Peterson and Jungck, 1988, Jungck et al., 2000). Instructors as well as students are collaborators in this three phase process, often providing additional insights and defining potential strengths and weaknesses in the design of the problem statement and the investigation. The resolution (or clarification) of the problem and its presentation extend opportunities for student practice in utilizing and evaluating scientific approaches to problem solving.

Visit http://bioquest.org/icbl to learn more and to gain access to the repository of cases developed by science instructors at the college and high school levels.

Support for the development of Investigative Case Based Learning was provided by NSF grant DUE 9952525 (Co-PIs Margaret Waterman, Southeast Missouri State University, and Ethel Stanley, BioQUEST and Beloit College) with additional support from the Howard Hughes Medical Institute, the Education Outreach and Training Partnership for Advanced Computational Infrastructure, and Engaging Partners In Cyberinfracstructure.

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This work is licensed by Margaret Waterman under a Creative Commons Attribution License (CC-BY 2.0), and is an Open Educational Resource.

Last edited by Margaret Waterman on Feb 14, 2008 11:52 am -0600.