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Journal of Computers in Mathematics and Science Teaching

2002 Volume 21, Number 3

Editors

Gary H. Marks

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Table of Contents

Number of articles: 5

  1. What Are Microcomputer-Based Laboratories (MBLs) for? An Example from Introductory Kinematics

    Ricardo Trumper, Haifa University, Israel; Moshe Gelbman, Physics Project—Tomorrow 98, Israel

    Teaching physics in the laboratory, and more specifically, the use of computers in the physics laboratory is a question of worldwide concern. In this article the authors shall try to validate the... More

    pp. 207-227

  2. The Effect of Web-Based Homework on Test Performance in Large Enrollment Introductory Physics Courses

    Jose Mestre, David M. Hart, Kenneth A. Rath & Robert Dufresne, University of Massachusetts, United States

    This study compares the effect of web-based homework (WBH) and paper-and-pencil homework (PPH) on student achievement as measured by exam performance. Various offerings of two large introductory... More

    pp. 229-251

  3. Learning Chemistry Through the Use of a Representation-Based Knowledge Building Environment

    Patricia Schank & Robert Kozma, SRI International, United States

    Many students leave high school chemistry courses with profound misunderstandings about the nature of matter, chemical processes, and chemical systems. The ChemSense project is addressing this... More

    pp. 253-279

  4. Computers for Mathematics: Theoretical Solution versus Constructive Solution

    M.r. Khadivi, Jackson State University, United States

    In this article the role of technology and its pivotal—in many cases, indispensable—role in solving real-world problems is underscored. It is demonstrated that the existence of a solution to a... More

    pp. 281-286

  5. Object-Oriented Implementation of the Backpropagation Algorithm

    Mohamed M. Khatib, Ain Shams University, Egypt; and Ismail. A. Ismail, Zagazig University, Egypt

    The Error-Backpropagation (or simply, backpropagation) algorithm is the most important algorithm for the supervised training of multilayer feed-forward Artificial Neural Networks (ANN). It derives ... More

    pp. 287-306