Pendekatan Deep Learning berbantuan Interacty untuk meningkatkan kemampuan pemahaman matematis berdasarkan self-confidence siswa

Abstract

Mathematical comprehension ability is essential, must be possessed by students to be able to construct concepts, connect various mathematical representations, and apply knowledge in problem solving. The Deep Learning approach supported by Interacty has the potential to create meaningful, reflective, and student-centered learning experiences that can strengthen conceptual understanding. However, the facts in the field show that students’ mathematical comprehension ability is still relatively low because learning tends to be procedure-oriented and does not utilize interactive technology optimally. The study aims to analyze students’ mathematical comprehension ability through a deep learning approach based on self-confidence. The quasi-experimental design study involved a sample of three grade VIII SMPN in Bandung City in the 2025/2026 academic year consisting of nine parallel classes. By random sampling, three classes were selected, namely VIII-E as experimental class I, VIII-D as experimental class II. And class VIII-F as a conventional class. The instrument used was a description test of students’ mathematical comprehension ability and a non-test self-confidence questionnaire. The results showed that students’ mathematical comprehension ability based on self-confidence, experimental class one ranked first, better than experimental class two and conventional class. The significance of the interaction between learning types and self-confidence on improving mathematical comprehension ability. The Deep Learning approach, supported by Interacty, can increase student engagement in the learning process through interactive, collaborative, and reflective activities, thereby improving students’ mathematical understanding.