Analisis Kompetensi Siswa Dalam Ilmu Robotika Menggunakan Mixed Methods
DOI:
https://doi.org/10.29408/jit.v8i1.28449Keywords:
Robotics Training, Technical and Analytical Skills, Industrial Revolution 4.0, Mixed Methods, SMK NWDI RencoAbstract
His study aims to evaluate the impact of robotics training on the technical and analytical skills of students at SMK NWDI Renco. The background of this research is based on the importance of developing technical and analytical skills in the era of the Industrial Revolution 4.0, where technology and automation have become integral parts of various industrial sectors. Vocational education at the high school level plays a strategic role in preparing a competent and adaptive workforce to meet technological advancements.The research method used is a mixed-method approach, combining both quantitative and qualitative methodologies. An experimental design was implemented with pre-test and post-test evaluations to measure changes in students' technical and analytical skills before and after the robotics training. Quantitative data were collected through specially developed technical and analytical skill tests, while qualitative data were gathered through in-depth interviews and Focus Group Discussions (FGDs) with students and teachers involved in the training.
The robotics training program was conducted over eight weeks, covering topics such as the fundamentals of robotics, programming, and practical applications through small-scale projects. Participants were purposively sampled and consisted of 30 eleventh-grade students from SMK NWDI Renco. A pre-test was conducted prior to the training to assess the students' baseline skills. During the training, direct observations were made to document students’ progress, and activities were documented. A post-test was conducted after the training to evaluate changes in technical and analytical skills. The results of the study showed a significant improvement in students’ understanding and skills. The average pre-test score was 63.2, while the average post-test score increased to 84.5. Specifically, the improvements included a 41.7% increase in the understanding of robotics concepts, a 35.4% improvement in microcontroller utilization, and a 24.6% improvement in sensor and actuator applications.
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