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Abstract

Errors in problem solving indicate that there are parts of the cognitive structure that are problematic, either because they are disorganized, disconnected or experiencing cognitive holes. thinking This research uses a qualitative approach. The data analysis technique used refers to the Miles and Huberman analysis model, namely data reduction, data presentation and finally drawing conclusions. The results showed that the three subjects studied were only S1 subjects who were able to answer all the questions given correctly without going through the defragmenting process, this was because the problem-solving thinking structure of the questions was classified as good, this success was supported by the level of cognitive understanding of the prerequisite material for the questions. good. While on the subject of S2, even though the conclusions of the answers already look correct, in one of the questions, it appears that there is an error in thinking which is categorized as pseudo, this error occurs because of the tendency to only pursue similarity questions and do not emphasize understanding mathematical concepts. The defragmenting efforts given are in the form of cognitive conflict interventions, scaffolding–restructuring and scaffolding explaning. While on the subject of S3, the low literacy ability in reading the test questions, as well as understanding the concept of the prerequisite material for the questions, causes errors at all stages of problem solving. The defragmenting efforts given are scaffolding-explaning intervention, scaffolding restructuring, cognitive conflict, scaffolding-review, disequilibration. The lack of knowledge about the prerequisite material so that more emphasis is placed on providing scaffolding-explaning.

Keywords

DefragmentingThinking StructureProblem SolvingPISA

Article Details

How to Cite
Wahab A, A., Buhaerah, Ahsan, M., & Busrah, Z. (2022). DEFRAGMENTING THE THINKING STRUCTURE OF PROBLEM SOLVING THROUGH COGNITIVE MAPPING BASED ON POLYA THEORY ON PISA PROBLEMS. Journal of Mathematics Learning Innovation, 1(1), 93–97. https://doi.org/10.35905/jmlipare.v1i1.3388
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