DEVELOPING A DEEP LEARNING-BASED INSTRUCTIONAL MODULE USING THE UNDERSTANDING BY DESIGN FRAMEWORK FOR LEARNING BIVARIATE DATA - Syelvira Nova Zulfaidany; Tiara Mastura Nafisa; Alifiani Alifiani (JME (Journal of Mathematics Education), 2026)
The development of the digital era has increased the need for statistical literacy and data interpretation skills as essential competencies for the 21st century. However, some students still have difficulty interpreting relationships between data. The application of the Understanding by Design (UbD) learning design framework and the deep learning approach in instructional modules has been widely carried out, but those that integrate both in the development of instructional modules for learning bivariate data are still limited. Therefore, the purpose of this study is to develop a valid and effective deep learning-based instructional module using Understanding by Design for learning bivariate data. The type of research conducted is research and development (RD) with the ADDIE development model (Analysis, Design, Development, Implementation, and Evaluation). The subjects of the study were 29 11th-grade students. Data collection was carried out using test and non-test techniques. The instruments used were validation sheets and pretest and posttest question sheets. Based on the validator's assessment, the developed instructional module produced a total average score of 3.40 which is included in the "valid" validity level. As for the results of the students' pretest and posttest, the developed instructional module was declared effective as assessed by an n-gain score of 0.32, which means there was an increase in learning outcomes between before and after the use of the developed instructional module with the criteria of "medium". Based on the results of the study, it can be concluded that the developed instructional module is valid and effective for use for learning bivariate data. These findings indicate that the integration of the deep learning approach and the Understanding by Design framework not only improves student learning outcomes but also helps students build a deeper conceptual understanding in interpreting data and understanding the relationships between variables meaningfully.