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Cell Biol Educ 4(3): 199-206 2005
DOI: 10.1187/cbe.04-11-0055
© 2005 American Society for Cell Biology
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ESSAY

Applying Instructional Design Theories to Bioinformatics Education in Microarray Analysis and Primer Design Workshops

Aviv Shachak*, Ron Ophir{dagger}, and Eitan Rubin{ddagger}

* The Department of Information Science, Bar-Ilan University, Ramat-Gan 52900, Israel; {dagger} The Bioinformatics and Biological Computing Unit, Department of Biological Services, The Weizmann Institute of Science, Rehovot, Israel;{ddagger} The Bauer Center for Genomics Research, Harvard University, Cambridge, MA 02138

Address correspondence to: Aviv Shachak (shachaa4{at}popeye.os.biu.ac.il).

The need to support bioinformatics training has been widely recognized by scientists, industry, and government institutions. However, the discussion of instructional methods for teaching bioinformatics is only beginning. Here we report on a systematic attempt to design two bioinformatics workshops for graduate biology students on the basis of Gagne's Conditions of Learning instructional design theory. This theory, although first published in the early 1970s, is still fundamental in instructional design and instructional technology. First, top-level as well as prerequisite learning objectives for a microarray analysis workshop and a primer design workshop were defined. Then a hierarchy of objectives for each workshop was created. Hands-on tutorials were designed to meet these objectives. Finally, events of learning proposed by Gagne's theory were incorporated into the hands-on tutorials. The resultant manuals were tested on a small number of trainees, revised, and applied in 1-day bioinformatics workshops. Based on this experience and on observations made during the workshops, we conclude that Gagne's Conditions of Learning instructional design theory provides a useful framework for developing bioinformatics training, but may not be optimal as a method for teaching it.

Key Words: instructional design theory • Gagne • conditions of learning • bioinformatics • graduate







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