International Journal of Mathematics and Mathematics Education (IJMME)
International Journal of Mathematics and Mathematics Education (IJMME) invites academics and researchers who do original research in the fields of Mathematics and Mathematics Education including but not limited to: Mathematics other relevant fields will also be considered: Pure Mathematics: Algebra, Number Theory, Geometry, Analysis, Topology, Logic, Discrete Mathematics, Combinatorics, Graph Theory. Applied Mathematics: Numerical Analysis, Mathematical Modelling, Dynamical Systems, Operational Research, Optimization, Financial Mathematics, Actuarial Mathematics, Computational Mathematics. Statistics: Mathematical Statistics, Multivariate Statistics, Time Series Analysis & Forecasting, Statistical Methods, Probability, Nonparametric Statistics, Circular Statistics, Spatial Analysis, Predictive Analytics, Prescriptive Analytics. Applied Statistics and Data Science: Big Data Analytics, Data Visualization, Statistical Modeling & Simulation, Data Mining, Machine Learning, Artificial Intelligence, Quality Control, Computational Statistics. Mathematics Education other relevant fields will also be considered: Innovative Mathematics Teaching and Learning applying various approaches such as Realistic Mathematics Education, Contextual Teaching and Learning, Project based Learning, Problem Based Learning, and many others. Mathematics Ability includes the following abilities: reasoning, connection, communication, representation, and problem-solving. A paper is eligible for this topic if it comprehensively discusses those abilities. Technology and Media in Mathematics Teaching and Learning the advance of information and communication technology (ICT) has been the concern of all human life, including in education. Assessment in Mathematics such as Mathematical Literacy, Critical Thinking, HOTS. Design/Development Research in Mathematics Education includes the following abilities STEM, STEAM, Ethnomathematics.
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<![CDATA[Research Trends in Higher-Order Thinking Skills in the journal Mathematics Education in Indonesia: from Design to Data Analysis ]]>
<![CDATA[Kania, Nia]]>;
<![CDATA[Kusumah, Yaya S.]]>;
<![CDATA[Dahlan, Jarnawi Afgani]]>;
<![CDATA[Nurlaelah, Elah]]>;
<![CDATA[Arifin, Zaenal]]>
<![CDATA[ International Journal of Mathematics and Mathematics Education (IJMME) Vol. 2 No. 3 (2024) ]]>
Publisher : <![CDATA[EDUPEDIA Publisher ]]>
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DOI: 10.56855/ijmme.v2i3.1048
<![CDATA[The study identified research trends in higher-order thinking skills in Mathematics Education in Indonesia, from Design to Data Analysis. The data came from content analysis of math education articles. As of April 2022, mathematics education articles in the Science and Technology Index (SINTA) are used solely. The Indonesian Ministry of Research, Technology, and Higher Education founded SINTA (http://sinta2.ristekdikti.go.id) to evaluate science and technology. This research shows that qualitative methods, such as theme analysis or grounded theory, can provide insight into student learning, while quantitative methods, such as regression or multivariate analysis, provide statistical analysis of HOTS factors. Recommendations include improving teaching, research, and policymaking on choosing the correct analysis method and institutional support. Diversification and data analysis are essential to improve HOTS research and support Indonesian education development.]]>
<![CDATA[The Effects of Virtual Algebra Tiles on the Performance of Year 8 Students in Solving Algebraic Linear Equations ]]>
<![CDATA[Alias, Nurul Hafizah]]>;
<![CDATA[Shahrill, Masitah]]>;
<![CDATA[Nurhasanah, Farida]]>;
<![CDATA[Adnan, Mazlini]]>
<![CDATA[ International Journal of Mathematics and Mathematics Education (IJMME) Vol. 2 No. 3 (2024) ]]>
Publisher : <![CDATA[EDUPEDIA Publisher ]]>
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DOI: 10.56855/ijmme.v2i3.1067
<![CDATA[This study aims to examine the effects of Virtual Algebra Tiles (VAT) on the performance of 41 Year 8 students in solving algebraic equations. The VAT used in this research is accessible and free through a website called Didax. Quantitative and qualitative data were collected from Class 8X (21 students) and Class 8Y (20 students) through convenient sampling in a public secondary school. The Didax VAT was used as an intervention tool to aid students in solving algebraic equations. Quantitative data, in the form of pre-test, post-test, and questionnaire, was administered to the students and analysed to determine the statistical significance of the findings. Qualitative data were used in focus group interviews to explore students’ perceptions of Didax VAT further. The conclusions of this study indicated that the result of the paired sample t-test was not significant, the intervention had a small positive effect on student learning. The qualitative data revealed most students positively perceived using Didax VAT in their learning.]]>
<![CDATA[Computational Thinking Readiness Level of First-Year Students of Mathematics Education ]]>
<![CDATA[Angraini, Lilis Marina]]>;
<![CDATA[Abdurrahman]]>
<![CDATA[ International Journal of Mathematics and Mathematics Education (IJMME) Vol. 2 No. 3 (2024) ]]>
Publisher : <![CDATA[EDUPEDIA Publisher ]]>
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DOI: 10.56855/ijmme.v2i3.1099
<![CDATA[The main objective of this study was to measure the level of computational thinking readiness in prospective first-year mathematics education students. In addition, this study also aims to identify factors that influence their level of readiness towards computational thinking. This research is qualitative and descriptive in nature. This study describes first-year mathematics education students' mathematical computational thinking ability based on the theory of mathematical computational thinking. This study was conducted on first-year mathematics education students in the academic year 2023/2024. There were 16 first-year mathematics education students, all of whom were taken as samples in this study, to obtain more in-depth information about the computational thinking ability of first-year mathematics education students for further research development. The instruments used to collect data on first-year mathematics education students' mathematical computational thinking ability are (1) a mathematical computational thinking ability test and (2) an interview. The data obtained were calculated using statistical tests, and the results will be explained in depth. The mean score of the first-year mathematics education student's computational thinking ability test was 59.68, indicating that students generally have a fairly good level of computational thinking ability.]]>
<![CDATA[Small Pseudo Principally Quasi-Injective Acts ]]>
<![CDATA[Abdul Kareem, Shaymaa Amer]]>
<![CDATA[ International Journal of Mathematics and Mathematics Education (IJMME) Vol. 2 No. 3 (2024) ]]>
Publisher : <![CDATA[EDUPEDIA Publisher ]]>
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DOI: 10.56855/ijmme.v2i3.1124
<![CDATA[This work aims to present a small pseudo principally quasi-injective acts as a novel generalization of pseudo principally quasi-injective S-acts over monoids. If each S-monomorphism from a small principal subact of an S-act MS into NS can be extended to S-homomorphism from MS into NS , an S-act NS is termed as a small pseudo principally M-injective. If an S-act MS is a small pseudo principally M-injective, it is called a small pseudo principally quasi-injective. This type of generalization has several properties. Additionally, the circumstances under which subacts inherit the Small Pseudo, principally quasi-injective acts, are studied. Examples are provided to demonstrate this concept. Small pseudo principally quasi-injective acts can coincide with small principally quasi-injective acts if certain criteria are met. We discuss new characterizations of small principally quasi-injective acts. The link between classes of small, principally quasi-injective acts with other classes of injectivity is shown.]]>
<![CDATA[Analyses of Pre-Service Teachers’ Errors in Solving Algebraic Tasks in Ghana. ]]>
<![CDATA[Oppong, Richard Asumadu]]>;
<![CDATA[Adjei, Emmanuel Antwi]]>;
<![CDATA[Amoah, Emmanuel Kojo]]>;
<![CDATA[Okuleteh, Enock]]>;
<![CDATA[Larri, Baba]]>
<![CDATA[ International Journal of Mathematics and Mathematics Education (IJMME) Vol. 2 No. 3 (2024) ]]>
Publisher : <![CDATA[EDUPEDIA Publisher ]]>
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DOI: 10.56855/ijmme.v2i3.1134
<![CDATA[This study analyzed prospective basic education teachers’ errors in solving algebraic tasks using Newman’s Error Analysis (NEA). The study employed an interpretivist philosophical paradigm with a qualitative case study design. Data were collected from 250 prospective mathematics teachers randomly selected from four colleges of education in the eastern region of Ghana using super item tests and unstructured interview protocols. Thematic analysis was employed to analyze the qualitative data. The study findings revealed that transformation and encoding errors among prospective mathematics teachers are the most prevalent errors. Other error types encountered included comprehension errors and process skills errors. In order to reduce aspiring mathematics teachers’ errors, the study emphasizes the necessity of focused interventions and professional development programs to address transformation and comprehension errors in algebraic concepts.]]>
