Dynamic visualization by GeoGebra for mathematics learning: a meta-analysis of 20 years of research

AbstractGeoGebra 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.Keywords: Dynamic visualizationGeoGebramathematics achievementmeta-analysis AcknowledgementsWe 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 statementNo potential conflict of interest was reported by the author(s).ContributorshipYing 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 statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by Hong Kong Research Grants Council, University Grants Committee (Grant No. 17605221).Notes on contributorsYing ZhangYing 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 WangPengjin 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 JiaWei 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 ZhangAijun 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 ChenGaowei 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.