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Water Level Forecasting Along The Texas Coast: Interdisciplinary Research With Undergraduates

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Conference

2005 Annual Conference

Location

Portland, Oregon

Publication Date

June 12, 2005

Start Date

June 12, 2005

End Date

June 15, 2005

ISSN

2153-5965

Conference Session

Engineers & Mathematicians Communicating

Page Count

9

Page Numbers

10.1458.1 - 10.1458.9

Permanent URL

https://peer.asee.org/14304

Download Count

27

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Paper Authors

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Zack Bowles

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Philippe E. Tissot

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Jeremy Flores

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G. Beate Zimmer

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Alexey L. Sadovski

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Carl Steidley

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Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Water Level Forecasting along the Texas Coast: Interdisciplinary Research with Undergraduates.

G. Beate Zimmer, Philippe E. Tissot, Jeremy S. Flores, Zack Bowles, Alexey L. Sadovski, Carl Steidley.

Texas A&M University–Corpus Christi, Corpus Christi, TX 78412.

Abstract:

While pure mathematics makes it sometimes difficult to involve undergraduates who have not yet completed the higher level math courses in research projects, research in applied mathematics is generally more accessible to these students. We present an example of an integrated research environment including faculty, research professionals and students which has facilitated the productive involvement of undergraduate students in applied mathematics research. The Texas A&M University-Corpus Christi Division of Nearshore Research manages a network of about 50 coastal observation stations including the Texas Coastal Observation Network. As part of the network operation a number of environmental time series are collected and archived leading to data analysis, quality control and modeling opportunities for applied mathematicians. This paper discusses an example of the results of the involvement of a student in an applied mathematics research project. The student is a freshman in mathematics who caught attention by doing well in Advanced Calculus. The student’s initial assignment is to investigate whether a change in the performance function used during the training of a neural network program will lead to significant changes in the accuracy of the water level forecasts produced by the model.

Introduction:

In its strategic plan1, the National Science Foundation (NSF) clearly states the importance of involving undergraduate students in research and other applied science activities “NSF is determined … that the process of learning does not end with the classroom. Meeting this goal requires efforts from all parts of the Foundation. The undergraduate level plays a pivotal role." Other programs such as the Sloan Foundation Scholarship Program2 also support this goal and there is a consensus amongst educators and funding agencies that promoting discovery-based learning is highly desirable in the undergraduate education. Even though research on learning styles and guidelines by funding agencies endorse undergraduate research, we find only a relatively small number of undergraduates in mathematics involved in research. The difficulty is likely inherent to the field of Mathematics. For example in order to prove new results in a field such as applications of Nonstandard Analysis to Functional Analysis, a student would need to be proficient in Topology, Model Theory, Functional Analysis and Nonstandard Analysis. If this knowledge basis is found in an undergraduate student, the student is usually ready to graduate or pursue graduate research. Not surprisingly not a single American undergraduate student has presented a paper at national and international conferences on Nonstandard Analysis since 1989.

Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright © 2005, American Society for Engineering Education

Bowles, Z., & Tissot, P. E., & Flores, J., & Zimmer, G. B., & Sadovski, A. L., & Steidley, C. (2005, June), Water Level Forecasting Along The Texas Coast: Interdisciplinary Research With Undergraduates Paper presented at 2005 Annual Conference, Portland, Oregon. https://peer.asee.org/14304

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