Data Analysis Techniques for Problem-Based Learning Model Development and Rewarding Elementary Linear Algebra

Mariam Nasution

Abstract


The problem in this research is how the data analysis techniques are used in developing problem-based learning models and giving elementary linear algebra rewards. The purpose of this study was to describe the results of data analysis in the research conducted. The type of research used is the type of R&D research with qualitative and quantitative methods. The stages of data collection carried out are: Preliminary Phase, Prototyping Phase and Assessment Phase. The Preliminary stage is processed descriptively, by clearly describing the results of the preliminary analysis in the form of needs analysis, curriculum analysis, concept analysis, and student characteristics analysis, each resulting analysis is equipped with conclusions. The prototyping stage uses instrument data that has been given to experts or validators to see the errors and accuracy of the instruments presented in the form of numbers. For the Assessment Phase, validity tests were carried out on the validity of the RPS and SAP instruments, model books, lecturer books, LKM, students' mathematical ability tests, attitude questionnaires and student learning interests. Product reliability tests were carried out on RPS and SAP instruments, model books, lecturer books, LKM, students' mathematical ability tests, attitude questionnaires and student learning interests. The product effectiveness test was obtained from the student's mathematical ability test data, attitude questionnaire and student learning interest questionnaire. Based on the analysis of the data used, it can be concluded that the product validity test results obtained high validity criteria. The results of the practicality test obtained high validity. The results of the effectiveness test are that the problem-based learning model and the provision of rewards can improve students' mathematical abilities and can encourage increased understanding of a concept and solve problems that are legitimate, efficient, and effective.

Keywords


Data Analysis; Learning Model; Linear Algebra.

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DOI: https://doi.org/10.24952/logaritma.v10i01.5676

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