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San Diego State University


Richard Levine

Rich is a statistician with research interests in educational data mining and learning analytics. His research team is currently developing data mining methods and algorithms to parse massive student information system and learning management system data to study student success in courses/programs, provide early alert systems for at-risk students, and assess pedagogical intervention strategies.  He serves on the SDSU Learning Analytics task force and recently completed a term as the Editor of the Journal of Computational and Graphical Statistics.  He is a Fellow of the American Statistical Association and served as a Fulbright Scholar to China.

Current Research Projects

Project Title: Reducing Bottlenecks and Improving Student Success in Large Enrollment Statistics Courses
Principal Investigators: Rich Levine and Kristin Duncan
Funder: CSU Chancellor’s Office
Award Amount and Dates: $105,856; 2014-2015

Project Description:
Project 1: Identify roadblocks in the curricular map of STEM majors extending the time-to-degree.  Develop an analytics framework to allow advisors to flag at-risk students and appropriately advise students as they progress to degree.  

Project 2: Assess learning communities on campus in terms of student success, retention rates and graduation rates.  Develop an analysis infrastructure for flagging at-risk students, develop appropriate intervention strategies, and refine existing learning communities.

Project Title: Action Research Project: Improving Time-to-Degree for STEM Students Changing Majors; Learning Community Analyses
Principal Investigators: Jeanne Stronach, Rich Levine, and Andy Bohonak
Funder: CSU Chancellor’s Office
Award Amount and Dates: $57,370; 2014-2015

Project Description:
Redesigning large enrollment, bottleneck introductory statistics course Stat 250 as a flipped classroom/blended learning environment.  Core statistics material will be delivered online and class time will be devoted to data analysis and simulation activity laboratories and active problem solving sessions.  Learning analytics component incorporated to assess the pedagogical reform against previous standard lecture offerings.

Project Title: Promising Course Redesign of Bottleneck Math/Stat Courses
Principal Investigators: Rich Levine, Janet Bowers, Chris Rasmussen
Funder: CSU Chancellor’s Office
Award Amount and Dates: $168,030; 2013-2014

Project Description:
Developing and assessing pedagogical innovations to improve student success and attainment of learning outcomes in three CSU recognized bottleneck, large enrollment courses: Pre-calculus, Calculus I, Elementary Statistics.


Associate Editor, Journal of Computational and Graphical Statistics, 2014-present

Member, Committee on Publications, American Statistical Association, 2015-present

Elected Member, SDSU Strategic Planning Initiative Learning Analytics Task Force, 2013-present

Recent Publications and Presentations

Note: Papers 4 and 8 are in statistics education.

1. Zablocki, R., Levine, R. A., Schork, A. J., Andreassen, O. A., Dale, A. M., Thompson, W. K. (2014). Covariate-Modulated Local False Discovery Rate for Genome-Wide Association Studies. Bioinformatics 30, 2098-2104.

2. Levine, R. A., Fan, J., Su, X-G., Nunn, M. E. (2014). Bayesian Survival Trees for Clustered Observations, Applied to Tooth Prognosis. Statistical Analysis and Data Mining 7, 111-124.

3. Hallett, M. J., Fan, J., Su, X. G., Nunn, M. E., and Levine, R. A. (2014). On Variable Importance Rankings for Correlated Survival Data, with Applications to Tooth Loss. Statistical Modeling 14, 523-547.

4. Richardson, G. M., Bowers, J., Woodill, A. J., Barr, J. R., Gawron, J. M., Levine, R. A. (2014). Topic Models: A Tutorial with R. International Journal of Semantic Computing 8, 85-98.

5. Levine, R. A., Sampson, E., and Lee, T. (2014).  Journal of Computational and Graphical Statistics.  WIREs Computational Statistics 6, 233-239.

6. Sharpsten, L., Fan, J., Barr, J. Su, X., Demirel, S., and Levine, R. A. (2013). Predicting Glaucoma Progression using Decision Trees for Clustered Data by Goodness of Split. International Journal of Semantic Computing 7, 157-172.

7. Su, X., Fan, J., Levine, R. A., Tan, X., and Tripathi, A. (2013). Multiple-Inflation Poisson Model with L1 Regularization. Statistica Sinica 23, 1071-1090.

8. Friedman, J., Bohonak, A., Levine, R. A. (2013). When Are Two Pieces Better than One, Fitting and Testing OLS and RMA Regressions. Environmetrics 24, 306-316.


 Rich Levine

  Richard Levine

  Professor of


Department of Mathematics and Statistics
San Diego State University
San Diego, CA 92182-7720