ERG SES E 02, Communities and Education
Statistical information is encountered in many areas of life and it is necessary to evaluate and interpret these statistical information (Koparan, 2013). NCTM (2000) (National Council of Teachers of Mathematics) has taken statistics and probability as content standards. It is considered as one of the learning domains in the curriculum with the name of data handling and its importance is emphasized by Turkish Ministry of Education(MoNE) (2017). The data handling learning domain involves learning outcomes that are closely related to the experiences of individuals who will help individuals to be more aware of the statistical data they will encounter in daily life. (MoNE, 2017).
For many years, the average concept in mathematics curriculum is defined as the measures of central tendency (median, mode and arithmetic mean). However, the average concept is mostly used in the same sense as the arithmetic mean concept and it is mostly taken from the procedural knowledge (Watson & Moritz, 2000). The average concept, which is a concept that provides information about the center of the distribution of a data group, is sometimes the most frequently repeating value (mode) in the data set, sometimes the middle value (median), and sometimes the balance point of the data set (arithmetic mean) (Uçar & Akdoğan, 2009). Arithmetic mean is one of the tools that gives information about the mean of data or its central tendency (Randall, 2006). Graphs are very important in statistical thinking and reasoning for data representation, regulation and analysis (Shaughnessy, 2007). At the same time, graphs help to understand better the relationships between data (Yılmaz & Ay, 2016) and has a serious importance in understanding verbal, numerical or algebraic expressions (Çelik & Sağlam-Arslan, 2012; Demirci & Uyanık, 2009).
Borko, Eisenhart, Brown, Underhill, Jones and Agard (1992) reported that content knowledge and pedagogical content knowledge are the basis of that field teaching. According to Ball (1990) teachers’ conceptual and procedural knowledge have to be correct and in addition, they must understand the principles underlying this information. In order to become a mathematics teacher, pre-service teachers should have a deep content knowledge, pedagogical content knowledge and knowledge about cognitive development of students (Shulman, 1986; Ball, 1990; Carpenter, Fennema & Franke, 1996).
Recent researches have shown that pre-service teachers' mathematical understanding in pre-university and university mathematics courses is not sufficient for teaching at primary level (Ball, 1990; Even, 1993; Tirosh, 2000; Toluk Uçar, 2009). It is important that the pre-service teachers who are trained to be teachers have efficent content knowledge. Therefore, in this study, middle school mathematics teachers’ content knowledge about data handling learning domain will be examined.
The research is carried out with 50 middle school pre-service teachers who were 3rd grades in 2018-2019 academic year. They are selected with easy-to-reach sampling method. The easy-to-use sampling method is a sampling method that gives researchers the speed and practicality used in situations where researchers cannot use other sampling methods (Şimşek & Yıldırım, 2013). Qualitative research method was used in the research. The qualitative method ensures that what is going on in a particular activity or situation and that people's attitudes and behaviors are explained in detail (Fraenkel, Wallen & Huyn, 2012). Consisting of prepared by expert opinion is used. In order to determine content knowledge about data handling learning domain of the pre-service teachers a questionnaire form was develop as measurement tool by researcher. This form was consisted of nine open-ended questions. Analysis of the study is still continuing. The results of the study will be shared during the presentation.
In recent years, it has been observed that the instructional explanations of teachers and pre-service teachers are memorable and procedural rather than understanding (Henningsen & Stein, 1997; Kinach, 2002). In addition, it was emphasized that the mathematical understanding of pre-service teachers is not sufficient to teach at primary level (Ball, 1990; Even, 1993; Tirosh, 2000; Toluk Uçar, 2009). Godino, Batanero, Roa and Wilhelmi (2014) emphasized that pre-service teachers in many countries have very limited statistical profiency and have very limited time to learn the necessary statistical knowledge and relevant pedagogical knowledge. The study is expected to show similar results with previous studies. It is expected to reach that pre-service teachers' content knowledge is not sufficient for their professional life as one of the finding of the study.
Ball, D. L. (1990). The mathematical understandings that prospective teachers bring to teacher education. The Elementary School Journal, 90(4), 449–466. Borko, H., Eisenhart, M., Brown, C. A., Underhill, R. G., Jones, D., & Agard, P. C. (1992). Learning to Teach Hard Mathematics: Do Novice Teachers and Their Instructors Give up Too Easily? Journal for Research in Mathematics Education, 23(3), 194-222. Even, R. (1993). Subject-matter knowledge and pedagogical content knowledge: Prospective secondary teachers and the function concept. Journal for Research in Mathematics Education, 24(2), 94–116. Fraenkel, J. R., Wallen, N. E. & Hyun, H. H. (2012). How to desing and evaluate research in education. (8.th ed.). New York: McGraw-Hill İnternational Edition. Henningsen, M., & Stein, M. K., (1997). Mathematical tasks and student cognition: Classroom-based factors that support and inhibit high-level mathematical thinking and reasoning. Journal for Research in Mathematics Education, 28, 524–549. Godino, J. D., Batanero, C., Roa, R. & Wilhelmi, M. R. (2014). Assessing and developing pedagogical content and statistical knowledge of primary school teachers through project work. Research Gate. Online: https://www.researchgate.net/publication/252794142 Kinach, B.M. (2002). A cognitive strategy for developing prospective teachers‟ pedagogical content knowledge in the secondary mathematics methods course: Toward a model of effective practice. Teaching and Teacher Education, 18(1), 51–71. Koparan, T. (2013). Examination of Statistical Thinking Models, Ilkogretim Online, 12(3), 730-739. Milli Eğitim Bakanlığı (MEB), (2017). Matematik Dersi Öğretim Programı (İlkokul ve Ortaokul 1, 2, 3, 4, 5, 6, 7 ve 8. Sınıflar). Talim ve Terbiye Kurulu Başkanlığı, Ankara Randall, G. (2006). An Exploration of Students’ Statistical Thinking. Teaching Statistics, 28(1), 17-21. Shaughnessy, J. , M. (2007). Research on Statistical Learning and Reasoning,In F. K. Lester (Ed.), Second Handbook of research on mathematics Teaching and Learning, 957-1009, Charlotte, NC: Information Age Publishing Shulman, L. S. (1986). Those Who Understand: Knowledge Growth in Teaching. Educational Researcher, 15(2), 4-14. Şimşek, H., Yıldırım, A. (2013). Sosyal bilimlerde nitel araştırma yöntemleri. 9.Baskı. Ankara: Seçkin. Tirosh, D. (2000). Enhancing prospective teachers‟ knowledge of children‟s conceptions: The case of division of fractions. Journal for Research in Mathematics Education, 31(1), 5–25. Watson, M. & Moritz, J. (2000). The Longitudinal Development of Understanding of Average, Mathematical Thinking and Learning, 2(1&2), 11-50. Yılmaz, N., Ay, Z.S. (2016). Investigation of 8th Grade Students’ Knowledge and Skills about Histogram. Elementary Education Online, 2016; 15(4): 1280-1298, [Online]:http://ilkogretim-online.org.tr
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