데이터 리터러시 관점에서 초등 수학과 성취기준 및 교과서 문제의 비구조성(ill-structured) 비교분석

Abstract

Objectives The purposes of this study were to analyze the achievement standards of elementary mathematics and curriculum in each country from the perspective of data literacy, compare and analyze the problem of ill-structured in math textbooks, and present the direction of future curriculum and textbook development.
 Methods To analyze the elementary mathematics curriculum standards in Korea, the United States, Japan, and India from a data literacy perspective, components of data literacy was selected and analyzed with a focus on whether the standards in each country's curriculum reflect the content of the components. After categorizing the standards, to analyze the cognitive level of the standards, the level of the standards is compared by the mathematics cognitive domain, which is an indicator for the development of the TIMSS 2019 assessment, and the problems ill-structured of in each country are examined in terms of realism, complexity, and openness.
 Results Compared to other countries, Korea did not deal with various aspects of data literacy in the curriculum standards, and showed a low proportion in the cognitive domain that requires high-dimensional thinking. The problems of ill-structured in textbook were practical in that it mimics the real situation and provides descriptions and information about the situation, but complexity and openness were relatively not revealed, confirming the need to develop meaningful textbook problems for learners in the future.
 Conclusions In the curriculum, it is necessary to emphasize various aspects and high-dimensional thinking on the perspectives and components of data literacy and to be guided in connection with ‘data ethics’. Mathematics textbooks should be able to utilize high-dimensional thinking skills through ill-structured status and aspects of practicality, complexity, and openness need to be systematically presented. Pre-service teachers and teachers also suggested that the development of training and programs to foster data literacy capabilities, cultivate background knowledge on ill-structured status, and support teaching and learning design be conducted as follow-up studies.