A Sustainable Model for Integrating Current Topics in Machine Learning Research Into the Undergraduate Curriculum

    Authors

    M. Georgiopoulos; R. F. DeMara; A. J. Gonzalez; A. S. Wu; M. Mollaghasemi; E. Gelenbe; M. Kysilka; J. Secretan; C. A. Sharma;A. J. Alnsour

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Educ.

    Keywords

    Curriculum development; integrated research and teaching; machine; learning; team teaching models; undergraduate research experiences; Education, Scientific Disciplines; Engineering, Electrical & Electronic

    Abstract

    This paper presents an integrated research and teaching model that has resulted from an NSF-funded effort to introduce results of current Machine Learning research into the engineering and computer science curriculum at the University of Central Florida (UCF). While in-depth exposure to current topics in Machine Learning has traditionally occurred at the graduate level, the model developed affords an innovative and feasible approach to expanding the depth of coverage in research topics to undergraduate students. The model has been self-sustaining as evidenced by its continued operation during the years after the NSF grant's expiration, and is transferable to other institutions due to its use of modular and faculty-specific technical content. This model offers a tightly coupled teaching and research approach to introducing current topics in Machine Learning research to undergraduates, while also involving them in the research process itself. The approach has provided new mechanisms to increase faculty participation in undergraduate research, has exposed approximately 15 undergraduates annually to research at UCF, and has effectively prepared a number of these students for graduate study through active involvement in the research process and coauthoring of publications.

    Journal Title

    Ieee Transactions on Education

    Volume

    52

    Issue/Number

    4

    Publication Date

    1-1-2009

    Document Type

    Article

    Language

    English

    First Page

    503

    Last Page

    512

    WOS Identifier

    WOS:000271490000006

    ISSN

    0018-9359

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