Mithun, ShamimaVickery, MorganLuo, Xiao2023-11-072023-11-072022-06Mithun, S., Vickery, M., & Luo, X. (2022). Flipped Instructional Design Factors in an Introductory and an Advanced Data Science Course. ASEE 2022 Annual Conference: Excellence Through Diversity, Minneapolis, MN.https://hdl.handle.net/1805/36964In this full research paper, we evaluate the flipped instructional designs of two undergraduate data science courses at a Midwestern university: an introductory course on database fundamentals and an advanced database design course. This study is built upon our prior work in which we identified a set of eight instructional design factors for effective flipped classrooms in the literature and assessed their efficacy with senior students. Our analysis relies on students’ course evaluations, self-reported survey data, focus group responses, course performance data, and instructor observation data to answer the following research questions: 1. How do the eight instructional design factors for effective flipped classrooms serve novice versus advanced data science students? 2. How should instruction in flipped classrooms be varied for novice versus advanced data science students? Our analysis indicates that novice data science students have different instructional needs and challenges compared to their senior peers, particularly in relation to activities that require peer collaboration and were unmoderated by the instructor. We share the results of our quantitative analysis of self-reported survey data in which students ranked the aforementioned instructional design factors based on their effectiveness for their learning and qualitative analysis which takes student comments (from a free-response survey and focus group data) and instructor observation data to contextualize these rankings and inform our instructional design recommendations. These recommendations address students differing academic and interactional needs within the classroom and are to be implemented within the introductory course in its next iteration: (a) group norming and standardization around expectations for communication/collaboration, (b) transparent disclosure of the learning objectives for each activity, (c) offering guidelines to support students in providing actionable peer feedback, and (d) introducing low-stakes peer evaluations. We conclude with a discussion on the general affordances of the flipped classroom model for both introductory and advanced data science instruction compared to traditional lecture-based approaches.en-USPublisher Policyinstructional designflipped classroomsdata science studentsConference proceedings