Application of a Deep Learning Approach to Mathematics Learning Outcomes in the Probability Unit for Ninth-Grade Students

Fitria Andyasti Rahayu Pranata (Mathematics Education Study Program, Faculty of Teacher Training and Education, Universitas Siliwangi)
Nani Ratnaningsih (Mathematics Education Study Program, Faculty of Teacher Training and Education, Universitas Siliwangi)
Satya Santika (Mathematics Education Study Program, Faculty of Teacher Training and Education, Universitas Siliwangi)

Kognitif: Jurnal Riset HOTS Pendidikan Matematika
Vol. 6 No. 3 (2026): July - September 2026

This study aimed to determine whether the mathematics learning outcomes of ninth-grade students on probability material after the implementation of a deep learning approach had achieved the Minimum Mastery Criterion (MMC). The study was motivated by the importance of developing students’ conceptual understanding, critical thinking skills, and active participation in mathematics learning through meaningful and student-centered instruction. The deep learning approach was expected to help students develop higher-oerder thinking skills, particularly in applying, analyzing, and evaluating probability concepts. This research employed a pre-experimental method using a One-Shot Case Study design. The population of the study consisted of all ninth-grade students at SMP Negeri 8 Tasikmalaya. The sample was selected using the cluster random sampling technique, in which one class was randomly chosen as the research sample. The instrument used in this study was a mathematics learning outcomes test on probability material developed based on cognitive indicators, namely C3 (applying), C4 (analyzing), and C5 (evaluating). The test was designed to measure students’ understanding and problem-solving abilities related to probability topics. The data were analyzed using descriptive and inferential statistics techniques. Descriptive statistics were used to describe students’ mathematics learning outcomes, while inferential statistics were applied to test the research hypothesis. The hypothesis testing technique used in this study was the One-Sample t-Test to determine whether the average score of students’ learning outcomes had achieved the established MMC score of 80. The results showed that the average mathematics learning outcome score of the students was 73, which was below the established Minimum Mastery Criterion. Therefore, it can be concluded that the mathematics learning outcomes of ninth-grade students on probability material after the implementation of the deep learning approach had not yet achieved the Minimum Mastery Criterion.