Latent Class Analysis of Students' Mathematics Learning Strategies and the Relationship between Learning Strategy and Mathematical Literacy

Abstract

This study investigated how various mathematics learning strategies affect the mathematical literacy of students. The data for this study were obtained from the 2012 Programme for International Student Assessment (PISA) data of Taiwan. The PISA learning strategy survey contains three types of learning strategies: elaboration, control, and memorization. To objectively classify students' learning strategies, we conducted a latent class analysis (LCA) to determine the optimal fitting latent class model of students' performances on a learning strategy assessment and to explore the mathematical literacy of students who used various learning strategies. The result of the LCA showed that, among the models of two to five classes, the model of four classes was the optimally fitted model. The results of this study provide crucial information for mathematics educators regarding the achievement-related and strategy-related outcomes of schooling. By combining information from the assessment of mathematical literacy and survey of learning strategies that predispose students to using their mathematical literacy, a more complete picture of mathematics teaching and learning emerges. The implications of these results are discussed.

Determining the Relationships between Selected Variables and Latent Classes in Students’ PISA Achievement

“…At the school level, the schools were clustered in four classes with a pattern similar to the student level. In their study, Lin and Tai (2015) used latent class analysis to determine which mathematics learning strategies are influential on the level of students" mathematics literacy in Thailand. In the study, the PISA 2012 mathematics achievement test items and mathematics learning strategy items were used.…”

“…Multilevel approaches adopted in these models can be grouped into predictive regression (Anıl, 2009;Gülleroğlu, Bilican Demir, and Demirtaşlı, 2014;Thorpe, 2006;Tomul and Çelik, 2009;Xu, 2006;Yıldırım, 2009), structural equation (Akyüz and Pala, 2010;Anıl, 2008;Özer and Anıl, 2011;Usta and Çıkrıkçı-Demirtaşlı, 2014), and hierarchical linear models (Akyüz, 2014;Atar, 2014;Atar and Atar, 2012;Geske et al, 2006;Tavşancıl and Yalçın, 2015). However, in the literature, there are only a limited number of studies which have evaluated student achievement as a latent variable and analysed it based on patterns identified in variables by creating homogenous sub-classes (Finch and Marchant, 2013;Lin and Tai, 2015). In this type of work, analysis is conducted by determining latent classes as well as including socioeconomic level (SEL) or learning strategies variables in the model to make direct classifications.…”

Development of Mathematics Teaching Materials Based on Realistic Mathematics Education and Literacy in Junior High School

“…This is because the assessment in PISA does not just ascertain whether students can reproduce knowledge; it also examines how well students can extrapolate from what they have learned and apply that knowledge in unfamiliar settings, both in and outside of school [3]. The PISA is triennial international survey, which aims to evaluate education systems worldwide by testing students' knowledge and skills in mathematics, reading and science [4].…”

The need analysis development of mathematics learning model based realistic mathematical education and literacy in junior high school

“…The objective of PISA is to evaluate education systems worldwide by testing students' knowledge and skills in math, reading and science (Lin and Tai, 2015). PISA assessment is useful in recognizing the level of students' math skills in some countries, as well as for understanding the strengths and weaknesses of the education system in the countries involved in PISA (Kusumah, 2011).…”