By Aimee Schwab-McCoy
Information
Introductory data science classes cover a range of topics, including data gathering, exploration, modeling, and visualization. However, data science is still a young discipline, which means little is known about which topics students particularly struggle with. This poster analyzes student data from Data Science Foundations: a series of interactive, web-native data science textbooks using Python or R. Activity metrics like average number of attempts, proportion of students giving up, and average time to completion, will be used to quantify student struggle. Struggle data from conceptual and programming-based activities will be aggregated from thousands of students at a cross-section of more than 50 colleges and universities to identify challenging topics in an intro data science course. Programming-specific content in Python or R will also be compared across versions. Although specific activities are limited to a single textbook series, challenging topics and lessons learned will apply broadly. Instructors teaching difficult topics may wish to incorporate more instruction or scaffolding based on recommendations from this poster.