Abstract
GeoGebra is an open-source software package for supporting mathematics teaching and learning. It enables a dynamic visualization approach that is beneficial for students’ mathematics learning. However, few studies comprehensively investigate the effectiveness of GeoGebra as a scaffolding tool to achieve dynamic visualization in mathematics since its release. To fill this gap, we carried out a meta-analysis of studies examining the use of GeoGebra software for students’ mathematics achievement since it was initially published in 2002 until 2022. Nineteen effect sizes were synthesized from fourteen studies in the last two decades, with a total of 1,334 participants. The analysis results demonstrated a positive medium-to-large effect (Hedges’s g = 0.653) of GeoGebra as a dynamic visualization tool for improving students’ mathematics achievement. Topic, treatment duration, and sample size were significant moderators of the effect size, suggesting that GeoGebra as a scaffolding for dynamic visualization is more effective when implemented with fewer participants (i.e. less than 50), for a short period (i.e. within four weeks), and in the topics of calculus and geometry than other conditions. Location, grade level, publication year, learning theory, group work, and student operation did not show significant moderating effects. Pedagogical implications of the findings for students, teachers, and educational researchers and practitioners are discussed.
Acknowledgements
We are sincerely grateful to the reviewers and editorial team for the comments that substantially improved the article. We also appreciate the comments provided by Prof. Carol K. K. Chan, Mr. Yang Tao, Dr. Yuyao Tong, and Dr. Liru Hu from the Faculty of Education, The University of Hong Kong.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Contributorship
Ying Zhang initiated the project, conducted the data analysis, and revised the draft. Pengjin Wang drafted the manuscript. Ying Zhang, Pengjin Wang, and Wei Jia collected and coded the data. Aijun Zhang and Gaowei Chen supervised the study, provided important ideas for the research and revised the draft. All authors read and approved the final manuscript.
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Notes on contributors
Ying Zhang
Ying Zhang is a PhD candidate in the Faculty of Education at The University of Hong Kong. His research interests address topics in technology-enhanced mathematics education, classroom discourse, teacher professional development, psychometrics, and international large-scale assessments.
Pengjin Wang
Pengjin Wang is a PhD candidate in the Faculty of Education at the University of Hong Kong. His primary research interests include learning sciences, technology-enhanced learning, teacher professional development, and teaching English as a second language.
Wei Jia
Wei Jia is a PhD candidate in the Faculty of Education at The University of Hong Kong. His research interests address topics in technology-enhanced STEM education, classroom discourse, and multiple representations.
Aijun Zhang
Aijun Zhang is with the Department of Statistics and Actuarial Science at the University of Hong Kong. His research interests include experimental design, interpretable machine learning, and educational data mining.
Gaowei Chen
Gaowei Chen is an associate professor at the Faculty of Education, the University of Hong Kong. His research interests cover learning sciences, dialogic teaching and learning, classroom discourse, teacher professional development, video visualization, learning analytics, mathematics education, and educational statistics.