IJIET 2020 Vol.10(6): 435-442 ISSN: 2010-3689
doi: 10.18178/ijiet.2020.10.6.1403

Predicting the Determinants of Dynamic Geometry Software Acceptance: A Two-Staged Structural Equation Modeling — Neural Network Approach

Chiu-Liang Chen

Abstract—This research examined the predictors of dynamic geometry software adoption by using GeoGebra as a case study. The proposed model incorporated basic predictors of the technology acceptance model such as perceived usefulness (PU), perceived ease of use (PEOU), and attitude toward usage (ATU), as well as predictors relating to students’ mathematics attitudes, namely self-confidence in mathematics, perceived value of mathematics (VAL), and enjoyment of mathematics. Data were collected from 175 students who had applied GeoGebra for their mathematics learning, and a two-stage hybrid structural equation modeling (SEM)–neural network approach was employed to test the proposed research model. First, the variables significantly influencing GeoGebra usage intention were identified through SEM. Subsequently, the identified predictors were ranked in terms of their relative influence by using a neural network model. The results showed that PU, PEOU, ATU, and VAL had significant effects on students’ behavioral intentions to use GeoGebra, and PU was the most significant predictor of students’ intentions to use GeoGebra in mathematics learning, followed by PEOU, ATU, and VAL. The results of this study could be useful for teachers to formulate effective strategies of integrating dynamic geometry software into their mathematics teaching.

Index Terms—Dynamic geometry software, GeoGebra, structural equation modeling, technology acceptance model.

The author is with National Taipei University of Business, Taiwan (e-mail: ccliang@ntub.edu.tw).

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Cite:Chiu-Liang Chen, "Predicting the Determinants of Dynamic Geometry Software Acceptance: A Two-Staged Structural Equation Modeling — Neural Network Approach," International Journal of Information and Education Technology vol. 10, no. 6, pp. 435-442, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (Since 2019), INSPEC (IET), EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
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