ASEE's Virtual Conference
Web-browsing histories, online newspapers, streaming music, and stock prices all show that we live in an age of data. Extracting meaning from data is necessary in many fields to comprehend the information flow. This need has fueled rapid growth in data science education aiming to serve the next generation of policy makers, data science researchers, and global citizens. Initially, teaching practices have been drawn from data science's parent disciplines (e.g., computer science and mathematics). This project addresses the early stages of developing a concept inventory of student difficulty within the newly emerging field of data science. In particular this project will address three primary research objectives: (1) identify student misconceptions in data science courses; (2) document students’ prior knowledge and identify courses that teach early data science concepts; and (3) confirm expert identification of data science concepts, and their importance for introductory-level data science curricula. During the first year of this grant, we have collected approximately 200 responses for a survey to confirm concepts from an existing body of knowledge presented by the Edison Project. Survey respondents are comprised of faculty and industry practitioners within data science and closely related fields. Preliminary analysis of these results will be presented with respect to our third research objective. In addition, we developed and launched a pilot assessment for identifying student difficulties within data science courses. The protocol includes regular responses to reflective questions by faculty, teaching assistants, and students from selected data science courses offered at the three participating institutions. Preliminary analyses will be presented along with implications for future data collection in year two of the project. In addition to the anticipated results, we expect that the data collection and analysis methodologies will be of interest to many scholars who have or will engage in discipline-based educational research.
© American Society for Engineering Education, 2020
Wertz, Ruth E. H.; Schmitt, Karl RB; Clark, Linda; Sandstede, Bjorn; and Kinnaird, Katherine M., "Crowdsourcing Classroom Observations to Identify Misconceptions in Data Science" (2020). Computer Science: Faculty Publications, Smith College, Northampton, MA.