Claim Missing Document
Check
Articles

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) Vol 11, No 1 (2026): JME (Jan - Jun)
Publisher : Universitas Sembilanbelas November Kolaka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31327/jme.v11i1.2788

Abstract

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.
Pengembangan Modul Ajar Matematika SMA Materi Regresi Linier dengan Pendekatan Deep Learning Berbasis Kerangka Understanding By Design Alfiatur Rosida; Annisa Putri Rahmawati; Alfina Lutfi Rahmawati; Alifiani Alifiani; Dwi Nurcahyo
Science and Education Journal (SICEDU) Vol 5 No 2 (2026): Science and Education Journal 2026
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/sicedu.v5i2.439

Abstract

Penelitian ini bertujuan untuk mengembangkan modul ajar matematika berbantuan LKPD berbasis deep learning menggunakan kerangka Understanding by Design (UbD) pada materi regresi linier yang valid dan praktis. Penelitian ini adalah pengembangan (Research and Development) dengan menggunakan model 4D (define, design, develop, dan disseminate). Namun, penelitian ini hanya dilaksanakan hingga tahap develop karena keterbatasan waktu penelitian. Subjek uji coba penelitian adalah 36 siswa kelas XI di salah satu SMA Kota Malang. Instrumen penelitian berupa lembar validasi modul ajar, lembar validasi LKPD, dan angket respons siswa. Hasil penelitian menunjukkan bahwa modul ajar memperoleh nilai rata-rata validasi sebesar 3,91 dengan kategori valid, sedangkan LKPD memperoleh nilai rata-rata 3,90 dengan kategori valid. Hasil uji kepraktisan menunjukkan persentase rata-rata sebesar 84,61% dengan kategori praktis. Modul ajar yang dikembangkan mampu menciptakan pembelajaran yang bermakna, berkesadaran, dan menyenangkan melalui integrasi kerangka UbD, pendekatan deep learning, dan model problem based learning.