Analysis of Student Learning Experiences in Sign Language Learning through a Deep Learning Approach Based on the Case-Based Learning Model

Abstract

Students in special education programs often face challenges in mastering sign language due to limited access to interactive and contextual learning experiences. Therefore, this study aimed to explore the implementation of sign language learning by integrating the Deep Learning approach, which emphasizes mindful, meaningful, and joyful learning, with the Case-Based Learning (CBL) model to provide a more holistic learning experience. Using a descriptive research design with a qualitative approach supported by quantitative data, the study involved 51 students who had completed a sign language course and one instructor from the Special Education Study Program. Data were collected through observation, semi-structured interviews, and a student survey consisting of 30 Likert-scale items representing the three dimensions of Deep Learning. The results revealed that the integration of Deep Learning and CBL fostered active student participation, reflective awareness, and contextual understanding, with high mean scores across the three dimensions: mindful (4.43), meaningful (4.54), and joyful (4.51). Qualitative findings further showed that students not only developed technical sign language competence but also enhanced their empathy, reflective thinking, and appreciation of communication within inclusive contexts. These results indicate that the combination of Deep Learning and CBL effectively supports the development of both cognitive and affective aspects of learning. The study contributes to the design of inclusive education curricula by emphasizing reflective, experiential, and socially grounded learning strategies that can better prepare future special education teachers.