Abstract: Purpose: Studying the scientific research output of universities to identify researchers' interests and potential challenges for future research is considered a crucial endeavor. Mathematical education has recently garnered attention from universities, leading to an increase in student numbers pursuing graduate studies in this field. To facilitate effective planning and policymaking, it is essential to review and analyze past research, examine mathematics education theses, and create a scientific map. This map will help us understand the distribution of topics, analyze influential research concepts, and identify research gaps. This study aims to create a scientific map and analyze word co-occurrences in mathematics education theses from seven prominent universities in Iran. Methodology: The current research aims to investigate the co-occurrence of keywords in master's and doctoral theses within the field of mathematics education at leading Iranian universities. This study utilizes a descriptive-analytical approach based on social network analysis indicators. The statistical population comprises 1,123 theses from various universities, including Shahid Rajaee Teacher Training University, Shahid Beheshti University, Ferdowsi University, Bahonar University, Chamran University, Islamic Azad University of Science and Research Branch, and Islamic Azad University of Zahedan Branch, covering the period from 2001 to 2022. Data collection involves creating a checklist that includes thesis information, followed by four stages: data preparation, keyword matrix formation, calculation of social network analysis indicators, creation of co-occurrence graphs, and generation of scientific maps using software such as Excel, Ravar Matrix, Cinetic, NetDraw, and VOSviewer. Findings: The research findings indicate that scientific output in the field of mathematics education at the university level has increased over time, peaking in 2011 and 2017. However, there has been a gradual decline in output since 2011, with a more pronounced decrease observed since 2020. Despite this decrease, the total number of doctoral dissertations has risen between 2017 and 2023, and there has been an increase in the number of theses produced since 2016. One of the most important indicators in social network analysis is centrality, which comes in various forms and indicates the positions of specific nodes within the network. In the present study, three types of centrality indicators, namely degree centrality, betweenness centrality, and closeness centrality, were utilized. The results indicate that mathematical performance is the most influential term among theses and has had access to a larger network. Following that, mathematical education, problem-solving, learning, and various teaching methods have influenced the subjects of theses more. The findings have shown that educational and learning keywords have a higher level of centrality and serve as mediators in communication and coherence among other terms, playing a vital role in information transfer. The results indicate that mathematical education learning and mathematical performance have the closest centrality and are linked with the least distance to other terms. Conclusion: The findings indicate a relative decline in the production of master's theses in mathematics education since the year 2020, attributed to the spread of the coronavirus and a decrease in university activities. An analysis of keywords in the thesis titles revealed that mathematics education had the highest frequency, followed by words like learning, mathematical performance, problem-solving, and various teaching methods, highlighting the significance of these concepts in research within the field of mathematics education. Examination of the educational levels of the theses also showed that nearly half of the educational research in Iran's mathematics education was conducted at the secondary school level (high school), followed by the middle school level (high school) and elementary school level, indicating a focus on school mathematics in educational research. In this regard, for future research in this field, further study on topics such as pre-school mathematics education, vocational and professional training, in-service courses, higher education, and postgraduate studies is recommended. Analysis of the co-occurrence relationships of vocabulary present in thesis titles reveals that mathematical performance is the most influential term and has a broader network. On the other hand, due to having the highest centrality indices, mathematical education, and learning are subjects that play the most prominent intermediary role among other thesis topics, establishing a more effective relationship and continuity among them. This implies that these topics have had the highest information transfer burden among the selected concepts by researchers, considering the nature of the mathematical education focus, which is centered on the way mathematical instruction and learning are defined. The results show that mathematical education, learning, and mathematical performance have the highest degree of centrality proximity, establishing minimal distance from other subjects. The co-occurrence mapping of vocabulary indicates that pairs of vocabulary such as content analysis-textbooks, problem solving-math performance, math education-learning, and math education-math performance have the highest levels of co-occurrence, reflecting researchers' approaches to mathematical education research topic selection. These findings suggest that most studies conducted using content analysis have focused on textbooks. [ABSTRACT FROM AUTHOR]
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