Richard Levine

Professor of Statistics
Department of Mathematics and Statistics
Center for Research in Mathematics and Science Education

Email

Primary Email: [email protected]

Building/Location

Geology Mathematics Computer Science - 565
6475 Alvarado Rd
#206
San Diego, CA 92120

Bio

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 is also collaborating with California data science educators to develop and expand articulated data science pathways, particularly for the successful community college transfer student.

He is a Fellow of the American Statistical Association, served as a Fulbright Scholar to China, served as Editor of the Journal of Computational and Graphical Statistics, and was Overall Scientific Program Chair of the 2019 Joint Statistical Meetings.

Appointments

Associate Chair and Coordinator of Statistics & Data Science, SDSU Department of Mathematics and Statistics, 2023-present
Associate Editor for Statistics of the Notices of the American Mathematical Society, 2021-present
Executive Board Member, Southern California Consortium for Data Science, 2023-present

Areas of Specialization

Machine Learning, Learning Analytics and Educational Data Mining, Monte Carlo Methods Including Markov Chain Monte Carlo, Bayesian Decision Theory, Sports Statistics

Grants

2023-2027, Principal Investigator, California Learning Lab Data Science Grand Challenge, Building Critical Mass for Data Science: Southern California Data Science Consortium; Mike Ludkovski, PD, $1,300,000.

2016-2022, Principal Investigator, NSF 1633130 BIGDATA: IA: Acting on Actionable Intelligence: A Learning Analytics Methodology for Student Success Efficacy Studies, $1,096,196

2017-2020, SDSU Data Champions Program, Faculty Advisor at Analytic Studies and Institutional Research

2015-2016, Co-Principal Investigator, CSU Chancellor’s Office, Action Research Projects: Improving Time-to-Degree for STEM student changing majors; Learning Community Analytics and Campus Data Readiness Projects $212,106

2013-2017, Principal Investigator, CSU Chancellor's Office, Promising Practices and Sustaining Success for Course Redesign: Statistical Principles and Practices, $266,628

Publications

Publications in Learning Analytics, 2021-2023

Thorp, J., Levine, R. A., Fan, J. (2023). Random forest of interaction trees for estimating individualized treatment regimes with ordered treatment levels in obser- vational studies. Journal of Data Science 21, 391-411.

Shao, L., Levine, R. A., Guarcello, M. A., Wilke, M. C., Stronach, J., and Fan, J. (2023). Estimating a dose-response relationship in quasi-experimental student success studies. International Journal of Artificial Intelligence in Education 33, 155-184. https://doi.org/10.1007/s40593-021-00280-0.

Levine, R. A., Rivera, P. E., He, Lingjun, Fan, J., and Bresciani, M. J. (2023). A learning analytics case study: On class sizes in undergraduate writing courses. Stat: 12e527. https://doi.org/10.1002/sta4.527.

Fan, J., Beemer, J., Yan, X., Levine, R. A. (2022). On machine learning methods for propensity score matching and weighting in educational data mining applications. Artificial Intelligence in STEM Education: The Paradigmatic Shifts in Research, Ed- ucation, and Technology. Auerbach/CRC Press, Chapter 18.

Shao, L., Ieong, M., Levine, R. A., Stronach, J., Fan, J. (2022). Machine learning methods for course enrollment prediction. Strategic Enrollment Management Quar- terly 10(2), 11-29.

Shao, L., Levine, R. A., Hyman, S., Stronach, J., Fan, J. (2022). A Combinatorial Optimization Framework for Scoring Students in University Admissions. Evaluation Review 46, 296-335.

Li, Luo, Levine, R. A., and Fan, J. (2022). Causal effect random forest of interaction trees for learning individualized treatment regimes with multiple treatment in observations studies. STAT 11:e457. DOI: 10.1002/sta4.457.

Wilke, M. C., Levine, R. A., Guarcello, M. A., and Fan, J. (2021). Estimating the optimal treatment regime for student success programs. Behaviormetrika 48, 309-343.

Autenrieth, M., Levine, R. A., Fan, J., and Guarcello, M. A. (2021). Stacked ensemble learning for propensity score methods in observational studies. Journal of Educational Data Mining 13(1), 24-189. https://doi.org/10.5281/zenodo.5048425.

Hillis, T., Guarcello, M. A., Levine, R. A., and Fan, J. (2021). Causal inference in the presence of missing data using a random forest based matching algorithm. Stat 10:e326. DOI: 10.1002/sta4.326.