Browsing by Author "Ernie L. Stitzinger, Committee Member"

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    An Algorithm for Computing the Perron Root of a Nonnegative Irreducible Matrix
    (2007-03-09) Chanchana, Prakash; Carl D. Meyer, Committee Chair; Ernie L. Stitzinger, Committee Member; Zhilin Li, Committee Member; Min Kang, Committee Member
    We present a new algorithm for computing the Perron root of a nonnegative irreducible matrix. The algorithm is formulated by combining a reciprocal of the well known Collatz's formula with a special inverse iteration algorithm discussed in [10, Linear Algebra Appl., 15 (1976), pp 235-242]. Numerical experiments demonstrate that our algorithm is able to compute the Perron root accurately and faster than other well known algorithms; in particular, when the size of the matrix is large. The proof of convergence of our algorithm is also presented.
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    Does using computer software make a difference in learning Geometric and Probability Concepts?
    (2008-12-03) Eley, Peter Madison; Lee V. Stiff, Committee Chair; Ernie L. Stitzinger, Committee Member; Karen F. Hollebrands, Committee Member
    ELEY, PETER MADISON. Does using computer software make a difference in learning Geometric and Probability Concepts? (Under the direction of Lee V. Stiff, PhD). The purpose of the research project was to see if using computer software in classroom instruction would help students learn geometric and probability concepts better. Geometer’s Sketchpad and Probability Explorer software were used as treatment instructional tools during a two-week summer Geometry and Probability course. Two groups of students were taught using computer software to determine if the software had an effect on student learning. The research method used a pretest/posttest growth measurement indicator. Both groups of students were given pretests about geometry and probability to determine their prior understanding of the subjects. The first group was given a computer software treatment for learning geometry while the second group was taught geometry using traditional classroom methods. After one week, the groups were switched and taught probability. The first group was now taught using traditional classroom methods and the second group used computer software to learn probability. When checking to see if a treatment effect exists, considerations are given for students’ prior knowledge of the subject matter and computer skills. After the completion of two weeks of instruction, students were given a post-test to determine negative or positive changes in their achievement. An analysis of the post-test scores using quantitative and qualitative methods indicated that students were better motivated by using computer software. However, results of the data analysis indicated that there was no significant growth in geometric or probabilistic performance by students who received the treatments.