Date on Master's Thesis/Doctoral Dissertation

8-2017

Document Type

Master's Thesis

Degree Name

M.S.

Department

Computer Engineering and Computer Science

Degree Program

Computer Science, MS

Committee Chair

Nasraoui, Olfa

Committee Member

Altiparmak, Nihat

Committee Member

Hieb, Jeff

Author's Keywords

data science; data science pipeline; visualization; exploratory factor analysis; educational data mining

Abstract

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.

Available for download on Thursday, February 08, 2018

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