Author(s):
Enhancing Data Science Education: Empowering Instructors and Evaluating Data Science Module Impacts on Student Dispositions
The Data Science Infusion into Undergraduate STEM Education (DIFUSE) project is a comprehensive initiative aimed at developing data science education by addressing the challenges faced in providing professional development for instructors and evaluating the impact of data science modules on students’ attitudes toward data science.
The need for the DIFUSE project stems from the growing importance of data science skills across various disciplines, coupled with the persistent challenge of effectively integrating these skills into undergraduate education. Despite the recognition of data science as a crucial skill set, there remains a gap in seamlessly incorporating it into curricula. The DIFUSE project bridges this gap in part by designing data science modules for introductory STEM and social science courses and offering comprehensive professional development workshops for instructors. The DIFUSE modules are crucial data science curriculum materials designed to provide students with a foundational understanding of data science. These modules, available on GitHub, offer students from various fields a comprehensive experience in data science, covering areas such as engineering, anthropology, environmental sciences, sociology, psychology, health studies, and interdisciplinary studies. The workshops are designed to equip instructors with the strategies and resources necessary to integrate data science concepts into their courses, enhancing the quality and relevance of STEM education.
The guiding questions are (1) how can instructor be empowered to integrate data science concepts into their existing curriculum, and (2) how do data science modules impact students’ dispositions in data science.
The anticipated outcomes of the DIFUSE project are multifaceted. First, the project aims to foster curriculum innovation by developing flexible and reusable data science modules that can be integrated into a variety of undergraduate courses. Second, participating instructors enhance their teaching practices and course design as they work iteratively on the module design process with the DIFUSE team. Third, the project contributes to the scholarship of teaching and learning by investigating the impact of data science education on student attitudes towards data science. Finally, we expect the integration of data science modules into courses will increase student interest, leading to higher career aspirations and self-efficacy levels, as well as more positive beliefs toward data science.
The broader impacts of the DIFUSE project extend beyond individual classrooms. By enhancing data science education, the project contributes to the development of a skilled workforce capable of addressing complex societal challenges. Moreover, the project helps create a more equitable educational landscape, ensuring that more students from a variety of fields gain exposure to high-quality data science education. In conclusion, the DIFUSE project represents a transformative effort to advance data science education through targeted professional development for instructors, course resource creation, and a comprehensive assessment of its impact on student outcomes.
Coauthors
Merve Kursav, Petra Bonfert-Taylor, Lorie Loeb, Scott Pauls, Laura Ray, Dartmouth College, Hanover NH
