<![CDATA[Quantitative data analysis is an important competency for Civil Engineering students in supporting learning, research, and the preparation of undergraduate theses. However, the utilization of statistical software such as SPSS in the Civil Engineering Study Program, Faculty of Engineering, Universitas Tanjungpura, has not yet been implemented optimally and systematically. This condition is influenced by the limited availability of practical guidelines, standardized training datasets, and systematically scheduled practice sessions. This activity aims to improve students’ competencies in quantitative data processing and analysis through the use of statistical software. The implementation methods included the development of a practical SPSS e-module, provision of training datasets, implementation of a Paired Sample T-Test practical session, development of assessment instruments, and evaluation through questionnaires and mini-projects. The e-module was systematically developed, covering data input, statistical analysis procedures, and interpretation of SPSS output. The evaluation involved 29 students from the Civil Engineering Study Program enrolled in the Statistics and Probability course in the second semester. The evaluation results showed that the majority of students understood the basic functions of SPSS, were able to perform simple data analyses, and responded positively to the use of SPSS in learning activities. A total of 22 students (75.9%) stated that they strongly understood that SPSS is used for statistical data analysis, while 18 students (62.1%) stated that they understood the basic steps for using SPSS. In addition, 22 students (75.9%) strongly agreed that the use of SPSS in learning activities had been implemented effectively and systematically. Therefore, the implementation of SPSS-based e-modules, practical sessions, and mini-projects can improve students’ understanding and skills in quantitative data analysis while supporting systematic, practical, and technology-based learning. The program also produced digital learning materials that can be used independently and continuously developed to support students’ research data analysis needs.]]>
