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 (Universitas Sembilanbelas November Kolaka, 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