Context-Based Learning for Elementary Students: Constructing Representativeness in Arithmetic Mean Using AiC Theory

This study aims to examine whether elementary school students can construct the concept of representativeness in arithmetic mean while solving context-based tasks designed according to the Abstraction in Context(AiC) theory. To achieve this, context-based tasks were developed to allow students to explore the representativeness in arithmetic mean independently of the algorithm for calculating it. The epistemological actions exhibited by students during task-solving were analyzed using the RBC model(Recognizing, Building-with, Constructing) of AiC theory. The findings reveal that students initially perceived the arithmetic mean merely as a computational result; however, through learning, they came to understand the arithmetic mean as a representative value of a data set that can be influenced by outliers and data distribution. This study highlights the significance of designing and utilizing context-based tasks in statistics education based on AiC theory, demonstrating how students' epistemological actions can be identified to facilitate their learning of statistical concepts.