Learning difficulties of introductory data science students
DOI:
https://doi.org/10.52041/iase2023.501Abstract
Introduction to Data Science (IDS) courses are being offered by many different departments either as a mandatory or an elective course. Because of the foundational nature of IDS courses to develop students’ understanding of data science, it is important to be aware of students’ potential learning difficulties. To that end, we conducted semi-structured interviews with 14 IDS instructors to study students’ difficulties. Qualitative content analysis was used to analyze the data. IDS instructors reported that students without prior coding experience encountered more syntactic difficulties than their peers. In terms of conceptual and strategic knowledge, students experienced difficulties in understanding principles of data visualization, the basics of coding, joining data sets, debugging and data wrangling. These findings suggest that IDS courses could be improved by addressing student difficulties and invite conducting future research with students to understand the dimensionality of student learning to improve capacity in data science (education).References
Asamoah, D. A., Doran, D., & Schiller, S. (2020). Interdisciplinarity in data science pedagogy: a foundational design. Journal of Computer Information Systems, 60(4), 370-377, https://doi.org/10.1080/08874417.2018.1496803
Bayman, P., & Mayer, R. E. (1988). Using conceptual models to teach BASIC computer programming. Journal of Educational Psychology, 80(3), 291, https://psycnet.apa.org/doi/10.1037/0022-0663.80.3.291
Danyluk, A., & Leidig, P. (2021). Computing competencies for undergraduate data science curricula: ACM data science task force. Peer-Reviewed Publications, 8, https://scholarworks.gvsu.edu/cispeerpubs/8
De Veaux, R. D., Agarwal, M., Averett, M., Baumer, B. S., Bray, A., Bressoud, T. C., ... & Ye, P. (2017). Curriculum guidelines for undergraduate programs in data science. Annual Review of Statistics and Its Application, 4, 15-30.
Frischemeier, D., Biehler, R., Podworny, S., & Budde, L. (2021). A first introduction to data science education in secondary schools: Teaching and learning about data exploration with CODAP using survey data. Teaching Statistics, 43(2021), S182– S189. https://doi.org/10.1111/test.12283
Jiang, S., Lee, V. R., & Rosenberg, J. M. (2022). Data science education across the disciplines: Underexamined opportunities for K‐12 innovation. British Journal of Educational Technology, 53(5), 1073-1079.
Heinemann, B., Opel, S., Budde, L., Schulte, C., Frischemeier, D., Biehler, R., ... & Wassong, T. (2018, November). Drafting a data science curriculum for secondary schools. Proceedings of the 18th Koli Calling International Conference on Computing Education Research, 1-5, https://doi.org/10.1145/3279720.3279737
McGill, T. J., & Volet, S. E. (1997). A conceptual framework for analyzing students’ knowledge of programming. Journal of Research on Computing in Education, 29(3), 276-297, https://doi.org/10.1080/08886504.1997.10782199
Merriam, S. B. (2009). Qualitative Research: A Guide to Design and Implementation. San Francisco: CA: Jossey-Bass.
Merriam, S. B., & Tisdell, E. J. (2016). Qualitative Research: A Guide to Design and Implementation (Fourth Edition). San Francisco.
Mike K. & Hazzan, O. (February 2023). What is data science? Communications of the ACM, 66(2), 12–13, https://doi.org/10.1145/3575663
National Academies of Sciences, Engineering and Medicine Consensus Report (2018). Data Science for Undergraduates: Opportunities and Options. Washington, https://nas.edu/envisioningds.
Pittard V. (2018). The Integration of Data Science in The Primary and Secondary Curriculum. Final Report: To the Royal Society Advisory Committee on Mathematics Education. https://royalsociety.org/-/media/policy/Publications/2018/2018-07-16-integration-of-data-science- primary-secondary-curriculum.pdf
Qian, Y., & Lehman, J. (2017). Students’ misconceptions and other difficulties in introductory programming: A literature review. ACM Transactions on Computing Education (TOCE), 18(1), 1- 24, https://doi.org/10.3102/0002831213477680
Sadler, P. M., Sonnert, G., Coyle, H. P., Cook-Smith, N., & Miller, J. L. (2013). The influence of teachers’ knowledge on student learning in middle school physical science classrooms. American Educational Research Journal, 50(5), 1020-1049.
Yan, D., & Davis, G. E. (2019). A first course in data science. Journal of Statistics Education, 27(2), 99-109, https://doi.org/10.1080/10691898.2019.1623136
