Radec Model Based on Google Form Decision Tree on Mathematical Concept Understanding - Harahap, Alya Rahmadani; Br Ginting, Siti Salamah (Desimal: Jurnal Matematika, 2026)
Students’ limited mathematical concept understanding remains a persistent challenge in mathematics education, particularly when classroom practices emphasize procedural performance rather than active knowledge construction and conceptual reasoning. This study aimed to examine the improvement of students’ mathematical concept understanding through the integration of the RADEC (Read, Answer, Discuss, Explain, Create) learning model with an adaptive digital pathway using Google Form Decision-Tree. A quantitative approach with a quasi-experimental pretest-posttest control group design was employed involving Grade X students at SMAN 1 Percut Sei Tuan, Indonesia. The participants were divided into an experimental class that received RADEC-based instruction assisted by Google Form Decision-Tree and a control class that received conventional mathematics instruction. Data were collected using a mathematical concept understanding test and analyzed through descriptive statistics, normalized gain analysis, prerequisite testing, and independent sample t-test. The results showed that students in the experimental class achieved higher conceptual improvement, with an average N-Gain score of 0.7153 categorized as high, compared with 0.5138 in the control class categorized as moderate. The independent sample t-test revealed a significant difference between the two groups (t = 4.185; p < 0.001). These findings provide empirical evidence that integrating active learning models with adaptive digital support can strengthen students’ conceptual development by facilitating structured knowledge construction and differentiated learning experiences. This study contributes to mathematics education research by extending the RADEC framework beyond conventional active learning implementation toward an adaptive technology-enhanced learning environment.