Title

Assessing And Evaluating Our Crcd Experiences At The University Of Central Florida: An Nsf Project

Abstract

Machine Learning has traditionally been a topic of research and instruction in computer science and computer engineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded to other disciplines, such as electrical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently, many undergraduate and first-year graduate students in the aforementioned fields do not have any exposure to recent research trends in Machine Learning. This paper reports on a project in progress, funded by the National Science Foundation under the program Combined Research and Curriculum Development (CRCD), whose goal is to remedy this shortcoming. In the past two years, we have reported on our experiences of introducing Machine Learning modules in sophomore and junior undergraduate classes, as well as our experiences of teaching two senior level Machine Learning classes, entitled Machine Learning I and Machine Learning II. In Machine Learning I we introduce our research to the students in the class. In Machine Learning II we assign research projects to the students and we help them produce their own contributions in the Machine Learning field. One important component of our project is the assessment and evaluation of our efforts. Last spring (spring of 2005) we have invited a CRCD Advisory Board consisting of academicians, and government/industry professionals, with expertise in Machine Learning, to a 1-day CRCD Symposium at the University of Central Florida to assess and evaluate the CRCD experience. This paper reports the results of the CRCD Assessment and Evaluation conducted by the CRCD Board. © American Society for Engineering Education, 2006.

Publication Date

1-1-2006

Publication Title

ASEE Annual Conference and Exposition, Conference Proceedings

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85029047563 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85029047563

This document is currently not available here.

Share

COinS