This master's thesis focuses on the analysis of students' incorrect solutions to tasks related to data processing in the Standards assessment tests. In the theoretical part, we present mathematical content related to data processing, interdisciplinary connections, mathematical literacy, and contextual tasks. We also provide a detailed overview of the representation of data processing in the curriculum across all three educational stages. Since our thesis deals with the analysis of student task-solving in the Standards assessment tests (NPZ), we also discuss other external assessments in mathematics.
In the empirical part we use qualitative analysis of students' task-solving on NPZs, which serves as a basis for further quantitative analysis; with this we identify the most common mistakes students make when solving data processing tasks and provide guidelines for addressing areas where students achieve the lowest results.
The analysis of the research results allows us to identify the most frequent mistakes that students make when solving data processing tasks, which in turn enables teachers to anticipate these errors when planning lessons on these topics.
The analysis results show that the majority of mistakes occurs due to conceptual confusion. Students know how to calculate individual measures of central tendency but mix up the concepts. One of the most common errors involves calculation mistakes, which suggests a lack of arithmetic knowledge, possibly also due to superficial reading of instructions. We recommend that teachers focus more on understanding individual measures of central tendency and the process of calculating probabilities when teaching data processing. Since these are fundamental skills such as arithmetic and reading instructions, we suggest that teachers pay special attention to them within other mathematical topics as well.
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