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Journal articles on the topic 'Mathematics, statistics'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 July 2024

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1

Ghatpande, Kalpana Abhay. "Innovative Teaching Strategies in Mathematics and Statistics." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 323–26. http://dx.doi.org/10.31142/ijtsrd20331.

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2

KRASNOZHON, O. B., and V. V. MATSIUK. "КОМП’ЮТЕРНО-ОРІЄНТОВАНІ ЕЛЕМЕНТИ НАВЧАННЯ МАТЕМАТИЧНИХ ДИСЦИПЛІН МАЙБУТНІХ УЧИТЕЛІВ МАТЕМАТИКИ." Scientific papers of Berdiansk State Pedagogical University Series Pedagogical sciences 1, no. 2 (October 4, 2021): 255–62. http://dx.doi.org/10.31494/2412-9208-2021-1-2-255-262.

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The article is devoted to the issues of constructing effective computer-oriented components of the methodological system of teaching the disciplines «Linear Algebra» and «Probability Theory with Elements of Mathematical Statistics» provided for in the educational and professional program «Secondary Education (Mathematics)» of the first level of higher education in the specialty 014 Secondary Education (Mathematics). The article analyzes the methodological aspects of the effective organization of computations when finding the angle between a given vector and a nonzero subspace of Euclidean space, as well as using the least squares method for processing experimental data. The theoretical and practical information known to students-mathematicians from the corresponding sections of these academic disciplines is briefly presented. Analyzed educational, methodological and scientific literature used in teaching linear algebra and probability theory with elements of mathematical statistics; the expediency of using computer-oriented elements of teaching mathematical disciplines of future mathematics teachers has been substantiated. The authors proposed the use of computer-oriented learning elements in the processing of the content of disciplines and the development of test tasks of different levels of complexity in linear algebra and probability theory with elements of mathematical statistics in order to objectively assess the level of students' knowledge and timely correct individual educational trajectories. The article provides examples of the application of computer-oriented elements of teaching linear algebra and probability theory with elements of mathematical statistics, and also analyzes the methodological features of the organization of calculations in the software mathematical environment Mathcad. The methodological and practical materials presented in the article can be useful for students to organize and activate independent scientific and pedagogical activities, teachers of secondary educational institutions, heads of optional and circle work of students, teachers of linear algebra and probability theory courses with elements of mathematical statistics of pedagogical higher educational institutions. Key words: methods of teaching mathematics, computer-oriented elements of teaching mathematics, linear algebra, probability theory, mathematical statistics, Euclidean space, non-zero subspace of Euclidean space, least squares method.

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3

Usiskin, Zalman. "Mathematical Modeling and Pure Mathematics." Mathematics Teaching in the Middle School 20, no. 8 (April 2015): 476–82. http://dx.doi.org/10.5951/mathteacmiddscho.20.8.0476.

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Common situations, like planning air travel, can become grist for mathematical modeling and can promote the mathematical ideas of variables, formulas, algebraic expressions, functions, and statistics.

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4

Capaldi, Mindy. "Mathematics Versus Statistics." Journal of Humanistic Mathematics 9, no. 2 (July 2019): 149–56. http://dx.doi.org/10.5642/jhummath.201902.10.

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5

Bailey, R. A. "Statistics and Mathematics: The Appropriate Use of Mathematics within Statistics." Journal of the Royal Statistical Society: Series D (The Statistician) 47, no. 2 (July 1998): 261–71. http://dx.doi.org/10.1111/1467-9884.00131.

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6

Salekhova, L. L. "PRE-SERVICE MATHEMATICS TEACHER’S ATTITUDE TO MATHEMATICAL STATISTICS." Современные наукоемкие технологии (Modern High Technologies), no. 3 2024 (2024): 167–71. http://dx.doi.org/10.17513/snt.39965.

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7

Auliya, Risma Nurul. "Can Mathematics and Statistics Perception Explain Students' Statistical Literacy?" JRAMathEdu (Journal of Research and Advances in Mathematics Education) 3, no. 2 (January 11, 2019): 86. http://dx.doi.org/10.23917/jramathedu.v3i2.5983.

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The aim of this research is intended to examine the effect of mathematics and statistics perception towards students’ statistical literacy. The research utilized a survey method with quantitative approach. The research population was undergraduate students from major informatics in one of university in Jakarta. The sample consisted of 80 students, 50 of students were male, while 30 of the students were female. All the participants belonged to similar grade level and enrolled in a statistics course. A survey instrument entitled “Mathematics and Statistics Perception Scale (MSPS)” was intended to examine undergraduate students’ perceptions of self-efficacy and their attitudes toward the application of statistics in real world settings. The initial scale consisted of 15 items and the final version consists of 11 items. A multiple-choice survey with 22 questions measuring statistical literacy skills and consisted of items measuring skills on interpreting or critiquing the prevalence of statistical phrases and ideas in the media. The data collected were analyzed using linear regression. The result of the analyses showed mathematics and statistics perception has an influence in the way students’ statistical literacy skills.

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8

Alexander, Gordon J., Andrew Adams, Della Bloomfield, Philip Booth, and Peter England. "Investment Mathematics and Statistics." Journal of Finance 49, no. 1 (March 1994): 359. http://dx.doi.org/10.2307/2329150.

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9

Booth, A. J., and A. Francis. "Business Mathematics and Statistics." Mathematical Gazette 71, no. 457 (October 1987): 252. http://dx.doi.org/10.2307/3616796.

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10

Glickman, L. V., and A. Francis. "Business Mathematics and Statistics." Mathematical Gazette 74, no. 467 (March 1990): 90. http://dx.doi.org/10.2307/3618893.

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11

McClure, Elizabeth A., Ralph Riddiough, and Deanne Thomas. "Statistics for Higher Mathematics." Mathematical Gazette 84, no. 500 (July 2000): 348. http://dx.doi.org/10.2307/3621705.

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12

Cobb, George W., and David S. Moore. "Mathematics, Statistics, and Teaching." American Mathematical Monthly 104, no. 9 (November 1997): 801. http://dx.doi.org/10.2307/2975286.

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13

Cobb, George W., and David S. Moore. "Mathematics, Statistics, and Teaching." American Mathematical Monthly 104, no. 9 (November 1997): 801–23. http://dx.doi.org/10.1080/00029890.1997.11990723.

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14

Tamba, Kimura Patar, and Wiputra Cendana. "The Relationship between pre-service elementary school mathematics teachers’ beliefs about epistemology of mathematics, teaching and learning, and mathematics assessment." Premiere Educandum : Jurnal Pendidikan Dasar dan Pembelajaran 11, no. 1 (June 1, 2021): 40. http://dx.doi.org/10.25273/pe.v11i1.8311.

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<div><p>This study aims to examine the relationship between epistemological beliefs, teaching-learning beliefs and assessment beliefs in mathematics education. This research is a quantitative study with a correlational study. Data collection using the survey method with a cross-sectional design. The participants were 71 pre-service elementary school , mathematics teachers. The data on beliefs were collected through means of a questionnaire. The data collected from the questionnaire were then analyzed quantitatively through descriptive and inferential statistics. Descriptive statistics utilizes the mean value, maximum value, and standard deviation values. Inferential statistics use the product-moment correlation as well as path analysis. The research results show that there is a positive and significant correlation between static and dynamic beliefs on epistemology of mathematics, and the constructivist beliefs on mathematics teaching and learning, with the productive beliefs on mathematics assessment. In addition, there is seen to be a functional influence between both epistimological beliefs (both static and dynamic), as well as beliefs on teaching and learning (constructivist) and beliefs about mathematic assessment (productive). The results of this research signify the importance of considering one’s beliefs about the epistemology of mathematics and mathematics teaching and learning when constructing their beliefs regarding mathematics assessment.</p></div>

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15

Pfanzagl, J. "MATHEMATICAL THEORY OF STATISTICS (de Gruyter Studies in Mathematics 7)." Bulletin of the London Mathematical Society 19, no. 4 (July 1987): 410–11. http://dx.doi.org/10.1112/blms/19.4.410.

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16

Mindarti, Lilik, and Sunismi Sunismi. "PENGEMBANGAN MODEL PEMBELAJARAN MATEMATIKA KNISLEY BERBASIS BRAINSTORMING MATERI STATISTIKA." JPM : Jurnal Pendidikan Matematika 1, no. 2 (April 16, 2015): 147. http://dx.doi.org/10.33474/jpm.v1i2.722.

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The aim of this development is to produce mathemathics Kinsley learning model of Brainstroming-based Statistics of the Tenth grade Senior High School. This study employed research and development design in which the researcher developed a mathematics Kinsley learning model of brainstorming-based statistics for the tenth grade senior high school students. This study operated the model development of Plomp with 4 developmental steps as follows 1) Preliminary Insvestigation, 2) Design, 3) Realisation, 4) Test, evaluation, and Revision. The instruments used were validation sheet, responses questionnaire, and observation sheet. 35 students of the class X IPA 4 SMAN 3 Malang were involved in this study. The results of this study were validated by two balidators; expert and practitionaire validators. User responses use to evaluate practicion of product. The validity from validators group and user obtain the score at 3,34, this mean the validity is significant. Then the result of development of mathemathics Knisley learning model of Brainstorming can be implementation on mathematic teaching on the class conventionally.

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17

Lee, Kyeong-Hwa, Eun-Sung Ko, and Byungjoo Tak. "A Review of Statistics Education Reform Efforts in Korea." Korean Society of Educational Studies in Mathematics - Journal of Educational Research in Mathematics 33, no. 3 (August 31, 2023): 479–505. http://dx.doi.org/10.29275/jerm.2023.33.3.479.

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Statistics education reform in Korea has played a critical role in innovating school mathematics education, as it provides students with skills necessary for a data-driven world. Over the past two decades, numerous researchers, teachers, and governmental support have been devoted to enhancing statistics education. The impact of these reform efforts has led to significant changes in the goals, learning content, structure, instructional methods, and assessment approaches in the national mathematics curriculum, mathematics textbooks, and mathematics classrooms. This article outlines key contributions to improving statistics education since the end of 1990s. It reviews the trend of statistics education research and government initiatives in developing a research-based statistics education system and highlights major changes in the national mathematics curricula and the mathematics textbooks: namely, the enhancement of real-world connection, the integration of statistical problem solving approaches, and the incorporation of technology in statistics education at all school levels. The article also presents prospects and challenges for continuing the reform of statistics education in Korea, based on lessons learned during the review of the reform efforts.

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18

Williamson, R. J., and I. Gwyn Evans. "Statistics for Advanced Level Mathematics." Statistician 34, no. 2 (1985): 258. http://dx.doi.org/10.2307/2988172.

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19

Watson, Rex, and I. Gwyn Evans. "Statistics for Advanced Level Mathematics." Mathematical Gazette 69, no. 447 (March 1985): 54. http://dx.doi.org/10.2307/3616462.

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20

Bridges, Richard, Anthony Eccles, Nigel Green, and Roger Porkess. "Statistics 2 (MEI Structured Mathematics)." Mathematical Gazette 78, no. 482 (July 1994): 219. http://dx.doi.org/10.2307/3618593.

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21

Williamson, Alan G., Anthony Eccles, Alan Graham, and Roger Porkess. "Statistics 1 (MEI Structured Mathematics)." Mathematical Gazette 79, no. 484 (March 1995): 183. http://dx.doi.org/10.2307/3620060.

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22

Bridges, Richard, Anthony Eccles, Nigel Green, and Roger Porkess. "Statistics 2 (MEI Structured Mathematics)." Mathematical Gazette 79, no. 484 (March 1995): 184. http://dx.doi.org/10.2307/3620061.

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Watson, Rex, Anthony Eccles, Nigel Green, and Roger Porkess. "Statistics 3 (MEI Structured Mathematics)." Mathematical Gazette 79, no. 484 (March 1995): 184. http://dx.doi.org/10.2307/3620062.

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24

Lord, Nick, and Alan Smith. "Practice for Advanced Mathematics: Statistics." Mathematical Gazette 84, no. 499 (March 2000): 156. http://dx.doi.org/10.2307/3621530.

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25

Roberts, Catherine A. "Mathematics and Statistics Awareness Month." Notices of the American Mathematical Society 64, no. 04 (April 1, 2017): 362. http://dx.doi.org/10.1090/noti1531.

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26

Pace, Nathan L. "Mathematics and Statistics in Anaesthesia." Anesthesia & Analgesia 89, no. 1 (July 1999): 269. http://dx.doi.org/10.1213/00000539-199907000-00082.

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27

Shafer, Steven L., and James C. Eisenach. "Mathematics and Statistics in Anaesthesia." Anesthesiology 91, no. 4 (October 1, 1999): 1181. http://dx.doi.org/10.1097/00000542-199910000-00045.

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Pace, Nathan L. "Mathematics and Statistics in Anaesthesia." Anesthesia & Analgesia 89, no. 1 (July 1999): 269. http://dx.doi.org/10.1097/00000539-199907000-00082.

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29

Bailey, David H., and Jonathan M. Borwein. "Experimental mathematics and computational statistics." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 1 (July 2009): 12–24. http://dx.doi.org/10.1002/wics.1.

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30

Colon-Berlingeri, Migdalisel, and Patricia A. Burrowes. "Teaching Biology through Statistics: Application of Statistical Methods in Genetics and Zoology Courses." CBE—Life Sciences Education 10, no. 3 (September 2011): 259–67. http://dx.doi.org/10.1187/cbe.10-11-0137.

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Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology. At our institution, we have made significant changes to better integrate mathematics into the undergraduate biology curriculum. The curricular revision included changes in the suggested course sequence, addition of statistics and precalculus as prerequisites to core science courses, and incorporating interdisciplinary (math–biology) learning activities in genetics and zoology courses. In this article, we describe the activities developed for these two courses and the assessment tools used to measure the learning that took place with respect to biology and statistics. We distinguished the effectiveness of these learning opportunities in helping students improve their understanding of the math and statistical concepts addressed and, more importantly, their ability to apply them to solve a biological problem. We also identified areas that need emphasis in both biology and mathematics courses. In light of our observations, we recommend best practices that biology and mathematics academic departments can implement to train undergraduates for the demands of modern biology.

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31

Sharkawi, Mahmoud. "Making Mathematics Accessible to Non-Mathematics Majors." Teaching Innovation Projects 9, no. 1 (March 3, 2020): 1–13. http://dx.doi.org/10.5206/tips.v9i1.10315.

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The purpose of this workshop is to present alternative strategies of instruction that will make the subjects of mathematics and statistics more accessible to students with non-mathematics backgrounds. It is not surprising that introductory mathematics and statistics courses can seem a little overwhelming and inaccessible to students with non-mathematics backgrounds. As a result, these students tend to feel distanced from the course material, or even discouraged from approaching instructors or teaching assistants (TAs) for help. The audience for this workshop includes graduate student TAs, post-doctoral fellows, instructors, lecturers, and anyone who wants to make mathematics and statistics a more engaging subject for students without the technical background. The focus of this workshop will be two-fold. First, we will examine how mathematics/statistics instructors can explain concepts to students of different backgrounds effectively via various role-play scenarios. Second, we will use the jigsaw technique to break up complex mathematical problems into pieces with the aim of encouraging collaboration and student engagement (Perkins & Saris, 2001). By attending this workshop, instructors will be able to help undergraduate students see mathematics as a more enjoyable learning experience that they can apply in their own respective fields. These two activities will help students with non-mathematical backgrounds feel more engaged with the material and become more confident when asking for help from an instructor or TA.

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32

Pillers Dobler, Carolyn. "Mathematical Statistics." American Statistician 56, no. 4 (November 2002): 332. http://dx.doi.org/10.1198/tas.2002.s204.

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33

Martynov, G. V. "Probabilistic-statistical programs from ?applied statistics?" Journal of Soviet Mathematics 50, no. 3 (June 1990): 1643–84. http://dx.doi.org/10.1007/bf01096290.

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34

Jacobbe, Tim. "Mathematics Education Doctoral Students’ Attitudes Toward Statistics." Journal of Education and Training Studies 10, no. 4 (September 16, 2022): 140. http://dx.doi.org/10.11114/jets.v10i4.5564.

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This study examines mathematics education doctoral students’ perspectives of statistics. Several studies have been conducted regarding undergraduate students’ attitudes toward statistics during their experiences in introductory statistics courses (e.g, Gal, Ginsburg, & Schau, 1997; Wise, 1985). Far fewer studies have been conducted regarding teachers’ attitudes toward statistics (e.g, Begg & Edwards, 1999; Greer & Ritson, 1994). One area of research that has not been explored concerns mathematics educators’ and mathematics teacher education faculty members’ attitudes toward statistics. This area of research is especially important given the impediments statistics educators have encountered when trying to increase the depth and quality of statistics education in the K-12 setting. Results show there is a need to emphasize statistics more broadly in the preparation of mathematics education doctoral students.

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35

Rollings, Lois. "Talking Statistics: A reflection on some of the problems with statistical language." MSOR Connections 22, no. 3 (April 25, 2024): 5–9. http://dx.doi.org/10.21100/msor.v22i3.1476.

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For most of my life I managed to swerve statistics. I learned a little at school and studied some (very theoretical) statistics as part of my mathematics degree. As a teacher, in schools and then university, I did not teach anything beyond GCSE statistics. It was not until I got a post in Mathematics and Statistics support a decade ago that I had to begin to learn the subject properly.I had excellent support from the sigma network, attending a memorable SPSS Bootcamp and other events which helped me enormously. But I was conscious that there were various aspects of statistics that presented problems for me. One was the way in which statistics differed from mathematics in being much less cut and dried. If a student had made a mistake in a calculation or argument it was fairly easy to spot and correct. However, when a student said ‘my supervisor said I should do a t-test’ and this did not seem the most appropriate way forward it was much harder to advise. I also realised that I was finding the language of statistics harder to master than I felt it ought to have been. It is this aspect that I will focus on in this article as I tentatively suggest that students might also have such problems.

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36

Moore, David S., and George W. Cobb. "Statistics and Mathematics: Tension and Cooperation." American Mathematical Monthly 107, no. 7 (August 2000): 615. http://dx.doi.org/10.2307/2589117.

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37

Lula, Usman I. "Companion to Advanced Mathematics and Statistics." Journal of the Royal Statistical Society: Series A (Statistics in Society) 171, no. 4 (October 2008): 1035–36. http://dx.doi.org/10.1111/j.1467-985x.2008.00561_6.x.

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38

Devore, Jay. "Essential Mathematics and Statistics for Science." American Statistician 60, no. 4 (November 2006): 344–45. http://dx.doi.org/10.1198/tas.2006.s62.

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39

Paton, Ray. "Can Statistics Be Rescued From Mathematics?" Teaching Statistics 12, no. 3 (September 1990): 66–69. http://dx.doi.org/10.1111/j.1467-9639.1990.tb00118.x.

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Moore, David S., and George W. Cobb. "Statistics and Mathematics: Tension and Cooperation." American Mathematical Monthly 107, no. 7 (August 2000): 615–30. http://dx.doi.org/10.1080/00029890.2000.12005247.

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41

Rohana, Rohana, and Yunika Lestaria Ningsih. "STUDENTS’ STATISTICAL REASONING IN STATISTICS METHOD COURSE." Jurnal Pendidikan Matematika 14, no. 1 (December 31, 2019): 81–90. http://dx.doi.org/10.22342/jpm.14.1.6732.81-90.

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The role of statistics is wide and crucial in daily life, making statistics important. Many students have difficulty understanding statistics. This study aims to determine students' statistical reasoning about inference statistics, which is limited to the subject matter of the testing hypotheses about two-sample hypotheses testing. This study used descriptive research method. The subjects were 25 students of third-year Mathematics Education Departement at Universitas PGRI Palembang in the academic year 2018/2019. Data were collected through tests and interviews. Data were analyzed through descriptive quantitative. The results of data analysis showed that 32% of students had level 1 statistical reasoning (the lowest level), 20% were at level 2, 28% at level 3, 12% at level 4 and 8% at level 5 (highest level). Based on the result, it can conclude that students' statistical reasoning ability in learning statistical method is not satisfactory, students are still very lacking in reasoning.

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Stine, Robert, Martha L. Abell, James P. Braselton, and John A. Rafter. "Statistics with Mathematica." American Statistician 54, no. 2 (May 2000): 153. http://dx.doi.org/10.2307/2686038.

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43

Yang, Jing Mei. "Regression & Correlation Analysis of Higher Mathematics on Probability and Statistics." Applied Mechanics and Materials 556-562 (May 2014): 4167–70. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4167.

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Higher mathematics and Probability and statistics are the core course in university,There is a close relation between the two. By comparison and analysis of the scores of the two courses, this paper attempts to show that higher math scores on the impact of Probability and statistics, Higher mathematics X is in linear relationship with Probability and statistics Y,The coefficients for the two is r=0.541. the correlativity between higher mathematics and Probability and statistics has been investigated by regression analysis,the regression equation between the two is obtained,therefore the quantitative analysis can be carried out.Conclusion: There is a good linear correlation between higher mathematics scores and Probability and statistics scores.Description the quality of higher mathematics achievements on the impact of Probability and statistics can not be ignored, for the construction of Higher mathematics courses provide a strong basis.

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44

Rota, Gian-Carlo. "Mathematical basis of statistics." Advances in Mathematics 60, no. 1 (April 1986): 123. http://dx.doi.org/10.1016/0001-8708(86)90007-1.

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DUPUIS, DANIELLE N., AMANUEL MEDHANIE, MICHAEL HARWELL, BRANDON LEBEAU, DEBRA MONSON, and THOMAS R. POST. "A MULTI-INSTITUTIONAL STUDY OF THE RELATIONSHIP BETWEEN HIGH SCHOOL MATHEMATICS ACHIEVEMENT AND PERFORMANCE IN INTRODUCTORY COLLEGE STATISTICS." STATISTICS EDUCATION RESEARCH JOURNAL 11, no. 1 (May 31, 2012): 4–20. http://dx.doi.org/10.52041/serj.v11i1.337.

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In this study we examined the effects of prior mathematics achievement and completion of a commercially developed, National Science Foundation-funded, or University of Chicago School Mathematics Project high school mathematics curriculum on achievement in students’ first college statistics course. Specifically, we examined the relationship between students’ high school mathematics achievement and high school mathematics curriculum on the difficulty level of students’ first college statistics course, and on the grade earned in that course. In general, students with greater prior mathematics achievement took more difficult statistics courses and earned higher grades in those courses. The high school mathematics curriculum a student completed was unrelated to statistics grades and course-taking. First published May 2012 at Statistics Education Research Journal: Archives

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Uloku, Ossai Peter Agbadobi. "Mathematics and Gender Dichotomy Using Item Statistics." European Modern Studies Journal 7, no. 2 (May 29, 2023): 79–94. http://dx.doi.org/10.59573/emsj.7(2).2023.09.

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Issue: This study on mathematics and gender dichotomy with respect to item statistics adopted an ex-post facto research design. Methods: A sample of 92 final year students (46 males and 46 females) was drawn through purposive sampling technique from selected secondary schools in Delta State, Nigeria. A 50-item multiple choice test used by the West African Examinations Council (WAEC) in 2010 constituted the instrument for the study. Findings: The independent t-test analysis showed that the mean item statistics was statistically significant in favour of the male students. Conclusion and Recommendation: Mathematics items appeared easier to the males compared to the females. It was recommended that raw scores and item statistics should be used when determining gender distinctions in mathematics achievement to give room for comparison.

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47

Kader, Gary, and Mike Perry. "Power On!: Learning Statistics with Technology." Mathematics Teaching in the Middle School 1, no. 2 (September 1994): 130–36. http://dx.doi.org/10.5951/mtms.1.2.0130.

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In its Curriculum and Evaluation Standards for School Mathematics (1989), the National Council of Teachers of Mathematics recommends that the K-12 mathematics curriculum be broadened and designates statistics as an area deserving increased attention. The standards document promotes the concept that statistics be learned through the study of real problems with real data collected by the students. Rather than focus on developing formulas from which answers are simply computed, teachers should present statistics in a coherent fashion and develop the topic as a whole problem-solving process.

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48

Tamba, Kimura Patar. "Pre-service mathematics teachers’ understanding of vector." Jurnal Math Educator Nusantara: Wahana Publikasi Karya Tulis Ilmiah di Bidang Pendidikan Matematika 9, no. 1 (May 31, 2023): 15–24. http://dx.doi.org/10.29407/jmen.v9i1.18708.

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Vector is an important concept in mathematics. Pre-service mathematics teachers explore vector concepts in several courses. This concept is also related to various concepts in mathematics. The purpose of this study is to describe the pre-service mathematics teachers' understanding of vectors. This research is non-experimental research with a descriptive design. 67 pre-service mathematics teachers are participants in this study. Data were collected by survey method using the Test of Understanding of Vector instrument. Data analysis was carried out quantitatively using descriptive and inferential statistics. The descriptive statistics used are the average, maximum, minimum, and standard deviation. The inferential statistics used are t-test statistics. The results showed that in general, the pre-service mathematics teachers' understanding of vectors was in the medium category. The results also show that there is no difference in understanding of vectors between male and female pre-service mathematics teachers. However, in the two concepts, namely "unit vector" and "scalar multiplication" there are significant differences between males and females.

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Kurniawati, Risky, and Raekha Azka. "Development of Mathematics E-Comic to Facilitate the Mathematical Communication Ability." Hipotenusa : Journal of Mathematical Society 4, no. 2 (December 6, 2022): 165–84. http://dx.doi.org/10.18326/hipotenusa.v4i2.7501.

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This study aims to develop a valid mathematics e-comic learning media for class VIII students on statistical material. The design of the e-comic mathematics learning media development using the PPE stage includes planning (planning), production (production), and evaluation (evaluation). The criteria for achieving quality are carried out by testing so that they are declared valid by experts. The results showed that the e-comic learning media for class VIII students on statistics material for class VIII students on statistics material met the valid criteria. Product validity was obtained based on the assessment of experts which showed that the quality of the learning media was included in the very good category with an average score of 3.65 by the material expert validator, and was included in the very good category with an average score of 3.75 by the media expert validator. Based on the results of the assessment, it can be concluded that the e-comic learning media for class VIII students on statistical material is valid.

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Belsom, C. G. H. "Statistics in Mathematics Courses at A-Level." Mathematical Gazette 72, no. 460 (June 1988): 101. http://dx.doi.org/10.2307/3618916.

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