Date on Master's Thesis/Doctoral Dissertation
Computer Engineering and Computer Science
Computer Science, MS
Committee Co-Chair (if applicable)
data science; data science pipeline; visualization; exploratory factor analysis; educational data mining
This thesis presents an applied data science methodology on a set of University of Louisville, Speed School of Engineering student data. We used data mining and classic statistical techniques to help educational researchers quickly see the data trends and peculiarities. Our data includes scores and information about two Engineering Fundamental Class. The format of these classes is called an inverted classroom model or flipped class. The purpose of this study is to analyze the data in order to uncover potentially hidden information, tell interesting stories about the data, examine student learning behavior and learning performance in an active learning environment, including collaborative learning in a flipped classroom model.
Acun Sener, Asuman Cagla, "A data science pipeline for educational data : a case study using learning catalytics in the active learning classroom." (2017). Electronic Theses and Dissertations. Paper 2758.