Entrepreneurship Opportunities for SFSU CS students and case studies / A Data Mining Approach to Understanding Curriculum-Level Factors that Help Students Persist and Graduate

Date: 
Wednesday, September 25, 2019 - 17:30
Location: 
TH 331
Presenter: 
Dr. Isabel Hyo Jung Song & Dr. Hui Yang
Abstract: 

Entrepreneurship Opportunities for SFSU CS students and case studies (Isabel Hyo Jung Song)

During the talk, I would like to speak about the opportunities which SFSU CS students might be interested in, if they have a passion for entrepreneurship. My team with my students has applied for Venturewell, I Corps Node Program, CITRIS foundry and AngelHack. I would like to talk about the characteristics of each program and the success stories how my students got great experiences from there.

 

A Data Mining Approach to Understanding Curriculum-Level Factors  that Help Students Persist and Graduate (Hui Yang)

This study employed a host of machine learning approaches to study curriculum-level factors that can potentially affect the persistence and graduation outcomes of over 4,000 undergraduate students at San Francisco State University. Specifically, it addressed four questions: (1) how did the timing of students’ Mathematics courses affect their performance and outcome; (2) whether students who progressed farther through the prescribed foundation course sequences of the program exhibited higher persistence and graduation rates; (3) what were the most frequently taken sequences of courses, and whether students who progressed farther through those sequences exhibited higher graduation rates; and (4) whether greater progress was more important than other demographic and academic factors for predicting persistence and graduation. We found that students who took their Math course in the second year showed higher fifth-term and seventh-term persistence than students who took it in the first year. Also, students who progressed farther through their chosen or prescribed sequences consistently exhibited higher persistence and graduation rates. Furthermore, a student’s persistence was a more reliable predictor of graduation than other features. Overall, these findings can potentially inform an institution’s strategies for maximizing persistence and graduation by emphasizing a student’s progress through the curriculum.

Bio: 

Isabel Hyo Jung Song

An assistant professor of the Computer Science Department of San Francisco State University. Prior to joining SFSU, she worked for Samsung for around 20 years. She has a keen interest in transforming her consumer electronics experience in a big company into agile experience for young student entrepreneurs. Her research interests are with digital health, mobile, IoT, wireless technologies involved in big data analytics.

 

Hui Yang

Dr. Hui Yang received her Ph.D. degree in computer science from the Ohio State University with a specialization in data mining. She joined the Computer Science Department at San Francisco State University in 2006. Her main research interests are in the area of data mining and text mining, with a current focus on applications originated in the fields of education, biomedical informatics, and social networks. She has published 30+ research articles in international conferences and journals.