Abstract:

Teaching Data-Intensive Domain (DID) systems within computer science programs is increasingly critical for preparing students to address the complexities of modern technological and scientific challenges. These systems involve managing and analyzing large, heterogeneous datasets that are essential across domains such as healthcare, finance, environmental science, and engineering. However, teaching this subject is inherently challenging due to the broad scope of DID systems, the need to integrate data from multiple sources, and the steep learning curve associated with data analysis tasks. Educators must not only impart knowledge of data-management techniques but also develop students' abilities in data abstraction, modeling, and analytical thinking, all within the constraints of limited instructional time and without assuming prior domain expertise. To address this challenge in the classroom, we propose a novel pedagogical methodology based on the SILE method and its associated tool, Delfos, for educational support in teaching the data integration and analysis process in DID. The SILE method provides a structured approach that enables students to efficiently search for, identify, load, and exploit relevant data, helping them to develop a systematic understanding of the data-integration process. The Delfos tool complements this approach by automating repetitive and technical tasks, such as data preprocessing and cleaning, thereby allowing students to focus on the critical aspects of analysis, integration, and domain-specific exploration. To evaluate this approach, a practical activity was conducted with undergraduate Computer Science students at the Universitat Politècnica de València (UPV). Students were introduced to the SILE method and the Delfos tool and subsequently used them to integrate and analyze a domain-specific dataset. The activity encouraged dataset exploration, familiarity with its contents, and the application of analytical methods to extract meaningful patterns and insights. After completing the tasks, we carried out a detailed evaluation to assess the method’s and tool’s effectiveness in enhancing students' understanding and skills, as well as their overall satisfaction with the learning experience. The results were highly positive. A significant majority of students successfully completed the tasks, demonstrating not only a high level of proficiency in handling domain-specific data but also a marked improvement in their ability to navigate the integration and analysis process. Many students reported a strong sense of achievement and found the approach intuitive and accessible, including those without prior domain knowledge. Additionally, students rated the SILE method and Delfos highly in terms of usability, functionality, and perceived value for improving their capability to work with complex data. This experience demonstrates the potential of structured methodologies and targeted tools to enhance the teaching and learning of DID systems. The hands-on, student-centered approach fosters active learning and critical thinking, both essential for navigating modern data ecosystems. Overall, the findings suggest that this educational methodology can effectively bridge the gap between theoretical knowledge and practical application, equipping students with the skills they need to succeed in their future careers.

Keywords:

Data Intensive Domain, Teaching Methodology, Active Learning.