Authors retain the copyright without restrictions for their published content in this journal. HSSR is a SHERPA ROMEO Green Journal.
RELATIONSHIP AMONG STUDENTS’ FACING PROBLEM RELATED MATHEMATICS LEARNING AND LESSON
Corresponding Author(s) : Tomoko Nishikawa
Humanities & Social Sciences Reviews,
Vol. 7 No. 2 (2019): March
Purpose: The objective of this study is to examine the Japanese junior high school students’ feelings of difficulty in learning mathematics and discuss a possible solution on the ‘Lesson Study’ framework.
Methodology: A survey was performed on 616 students of a public junior high school regarding their feelings towards learning mathematics. The survey was carried out at the end of the academic year 2016 in March, and 182 students (44 first-year, 75 second-year, 63 third-year) participated in the study. In this investigation, only those who answered “No Problem” were further scrutinized by means of conjoint analysis for their anxieties towards the specific learning modules following the education system guidelines and classified as ‘Algebra calculations’, ‘Functions’ and ‘Geometrical figures’. Basically, the analysis consisted of assessing these students’ awareness of ‘being good at’ and ‘being not good at’ one specific module relative to the other modules. Data processing and conjoint analysis were performed with Microsoft Excel.
Main Findings: Roughly 68% of first year students, 77% of second year female students, and 72% of third year female students felt ‘Not Good At’ towards ‘Algebra calculation’ and ‘Functions’, whereas about 95% of second year male students self-assessed themselves as ‘Not Good At’ towards ‘Functions’, and 79% of third year males as ‘Good At’ towards ‘Algebra calculation’. Thus, even though some students declared “No Problem”, they were actually ‘Not Good At’.
Implications: These findings suggest that a class division according to the students’ feelings with ‘Lesson Study’ at the teachers’ level would help struggling students to learn mathematics. In addition, we showed that the use of conjoint analysis-based assessment may help educators and teachers to figure out students’ feelings towards learning mathematics.
Novelty: The use of conjoint analysis to analyze the students’ implicit feelings towards learning mathematics is followed by a discussion on the grounds of ‘Lesson Study’, for which a cycle at the individual level is presented.
Arani, M. R. S., Fukaya, K., & Lassegard, J. P. (2010). “Lesson Study” as Professional Culture in Japanese Schools: An Historical Perspective on Elementary Classroom Practices. Nichibunken Japan Review, 22, 171–200.
Doig, B., & Groves, S. (2011). Japanese lesson study: Teacher professional development through communities of inquiry. Mathematics Teacher Education and Development, 13(1), 77–93.
Ebaeguin, M., & Stephens, M. (2016). Key Elements of a Good Mathematics Lesson as Seen by Japanese Junior High School Teachers. In Proceedings of the 39th annual conference of the Mathematics Education Research Group of Australasia. 206–213.
Farhoush, M., Majedi, P., & Behrangi, M. (2017). Application of Education Management and Lesson Study in Teaching Mathematics to Students of Second Grade of Public School in District 3 of Tehran. International Education Studies, 10(2), 104-113.
Fujii, T. (2014). Implementing Japanese Lesson Study in Foreign Countries : Misconceptions Revealed. The Mathematics Teacher Education and Development Journal, 16(1), 65–83.
Fujii, T. (2015). The Critical Role of Task Design in Lesson Study. In A. Watson & M. Ohtani (Eds.), ICMI Study 22: Task design in mathematics education. Switzerland: Springer. 273–286.
Isoda, M. (2010). Lesson Study: Japanese Problem Solving Approaches. In APEC Conference on Reaplicating Exemplary Practices in Mathematics Education, Koh Samui.
Izuta, G. & Nishikawa, T. (2017). Assessing the Sense of ‘Good at’ and ‘Not Good At’ towards Learning Topics of Mathematics with Conjoint Analysis. AIP Conference Proceedings 1847, 030014-1–030014-7. doi:10.1063/1.4983892.
Izuta, G., Nishikawa, T., & Nakagawa, M. (2016). A Conjoint Analysis-Based Grouping Strategy for a Metacognitive Study Aimed to Assess Students’ Feeling of Difficulty towards Learning, In Conference Proceedings of 2016 Hong Kong International Conference on Education, Psychology and Society (5th HKICEPS), CD-ROM Format, Hong Kong, China, December 14-16, 189–200.
Kadroon, T., & Inprasitha, M. (2013). Professional Development of Mathematics Teachers with Lesson Study and Open Approach: The Process for Changing Teachers Values about Teaching Mathematics. Psychology, 4(2), 101–105.
Lewis, C. C. (2013). How Do Japanese Teachers Improve their Instruction? Synergies of Lesson Study at the School, District and National Levels. Commisioned Paper: The National Research Council Board on Science Education.
Lu, P. C., & Lee, P. Y. (2012). A Singapore Case of Lesson Study. The Mathematics Educator, 21(2), 34–57.
Nishikawa, T. & Izuta, G. (2017). A Characterization of Junior High Students with Anxieties towards Learning Mathematics, In Proceedings of 2017 International Conference on Education, Psychology, and Social Sciences, CD-ROM Format, Bangkok, Thailand, August 2–4, 404–413.
Nishikawa, T. & Izuta, G. (2018). A Look into the ‘No Problem’ Feeling Towards Studying Major Areas of Mathematics Among Japanese Junior High School Students. Bulletin of Yonezawa Women’s Junior College. 54, 103-110.
Orme, B. K. (2014). Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research (third edition), Research Publishers, Chicago, IL.
Perry, R. R., & Lewis, C. C. (2009). What is successful adaptation of lesson study in the US?. Journal of Educational Change, 10(4), 365–391.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/. (accessed 2019-05-08).
Takahashi, A. (2006). Characteristics of Japanese Mathematics Lessons. Tsukuba Journal of Educational Study in Mathematics, 25(1997), 37–44.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. Retrieved from https://ggplot2.tidyverse.org/. (accessed 2019-05-08).
Wickham, H. (2017). tidyverse: Easily Install and Load the ‘Tidyverse’. R package version 1.2.1. Retrieved from https://www.tidyverse.org/. (accessed 2019-05-08).
Wickham, H. (2018). scales: Scale Functions for Visualization. R package version 1.0.0. Retrieved from https://www.R-project.org/. (accessed 2019-05-08).
Wilke, Claus O. (2019). cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R package version 0.9.4. Retrieved from https://www.R-project.org/. (accessed 2019-05-08).