Asee peer logo

Using Neural Networks To Motivate The Teaching Of Matrix Algebra For K 12 And College Engineering Students

Download Paper |

Conference

2007 Annual Conference & Exposition

Location

Honolulu, Hawaii

Publication Date

June 24, 2007

Start Date

June 24, 2007

End Date

June 27, 2007

ISSN

2153-5965

Conference Session

Project and Model-Based Mathematics

Tagged Division

Mathematics

Page Count

15

Page Numbers

12.1557.1 - 12.1557.15

Permanent URL

https://peer.asee.org/1645

Download Count

71

Request a correction

Paper Authors

biography

Sharlene Katz California State University-Northridge

visit author page

Sharlene Katz is Professor in the Department of Electrical and Computer Engineering at California State University, Northridge (CSUN) where she has been for over 25 years. She graduated from the University of California, Los Angeles with B.S. (1975), M.S. (1976), and Ph.D. (1986) degrees in Electrical Engineering. Recently, her areas of research interest have been in engineering education techniques and neural networks. Dr. Katz is a licensed professional engineer in the state of California.

visit author page

biography

Bella Klass-Tsirulnikov Sami Shamoon College of Engineering (formerly Negev Academic College of

visit author page

Bella Klass-Tsirulnikov is a senior academic lecturer at Sami Shamoon College of Engineering, Beer Sheva, Israel (former Negev Academic College of Engineering). She accomplished mathematics studies at Lomonosov Moscow State University (1969), received Ph.D. degree in mathematics at Tel Aviv University (1980), and completed PostDoc studies at Technion - Israel Institute of Technology (1982). From 1995 she also holds a Professional Teaching Certificate for grades 7 – 12 of the Israeli Ministry of Education. Dr. Klass-Tsirulnikov participates actively in the research on functional analysis, specializing in topological vector spaces, as well as in the research on mathematics education at different levels.

visit author page

Download Paper |

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

Using Neural Networks to Motivate the Teaching of Matrix Algebra for K-12 and College Engineering Students

Abstract

Improving the retention of engineering students continues to be a topic of interest to engineering educators. Reference 1 indicates that seven sessions at the 2006 ASEE Annual Conference were devoted to this subject. In order to be successful in an engineering program, it is recognized that students must have a solid background in mathematics. Studies have shown that students will be more motivated to study and learn mathematics if abstract mathematical concepts are presented in the context of interesting examples and applications2 – 5. To become appealing and relevant, abstract mathematical concepts should be connected to engineering and real life issues, as suggested by the guidelines of ASEE Engineering K-12 Centre6.

In two previous papers7 - 8 the co-authors have presented methods for improving the teaching of important mathematical concepts to K-12 and college level engineering students. In reference 7, the co-authors provided a method of teaching the concept of infinity that combines a rigorous development of the concept of infinity in freshman level mathematics courses for engineering students and an intuitive approach to infinity with hands-on exercises for K-12 students. In reference 8, the co-authors developed materials on topics from number theory, essential to the field of data security and suitable for K-12 students, as well as for remedial or preparatory courses for engineering freshmen.

This paper represents the third part in this continuing project of developing methods for improving the teaching and learning of mathematical concepts for engineering students. It presents an interesting context in which to teach simple matrix algebra, developing practical applications that can be used for both K-12 and college level algebra courses. The main application demonstrated in this paper is the design of a character recognition system, using a simple neural network. It leads to a series of interesting exercises with practical applications. The authors contend that these applications will motivate students to practice matrix operations which otherwise may seem tedious and to further motivate them to focus on their mathematics courses.

I. Introduction

The teaching of matrix algebra takes place in a variety of courses. In K-12, students are typically introduced to matrix operations in their second year algebra courses and/or pre-calculus. In college, students come across matrix algebra in pre-calculus, linear algebra, differential equations and linear systems courses. In lower division college mathematics, the widely used application of matrix operations is typically the solution of simultaneous equations, based on the fundamental idea of viewing a system of linear equations as a product of a matrix and a vector (see for example, section 1.4 of reference 9).

It is not until the upper division engineering courses that students see interesting practical applications of linear algebra. The standard college textbooks on linear algebra provide application models of matrix algebra and linear systems used in economics, computer graphics

Katz, S., & Klass-Tsirulnikov, B. (2007, June), Using Neural Networks To Motivate The Teaching Of Matrix Algebra For K 12 And College Engineering Students Paper presented at 2007 Annual Conference & Exposition, Honolulu, Hawaii. https://peer.asee.org/1645

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2007 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015