Richard LevineRich 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 on the Data Analytics and Reporting Working Group. 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.Recent PublicationsPelaez, K., Levine, R. A., Guarcello, M. A., and Fan, J. (2019). Latent Class Analysis and Random Forest Ensemble to Identify At-Risk Students in Higher Education. Journal of Educational Data Mining 11, 18-46. He, L., Levine, R. A., Bohonak, A. J., Fan, J., and Stronach, J. (2018). A Predictive Analytics Pipeline for Student Success Efficacy Studies. Applied Artificial Intelligence 32, 361-387 Beemer, J., Spoon, K., He, L., Fan, J. and Levine, R. A. (2018). Ensemble Learning for Estimating Individualized Treatment Effects in Student Success Studies. International Journal of Artificial Intelligence in Education 28, 315-335. Beemer, J., Spoon, K., Fan, J., Stronach, J., Frazee, J. P., Bohonak, A. J., Levine, R. A.* (2018). Assessing Instructional Modalities: Individualized Treatment Effects for Personalized Learning. Journal of Statistics Education 26, 31-39. He, L., Levine, R. A., Fan, J., and Stronach, J. (2018). Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research. Practical Assessment, Research, and Evaluation 23 (1). Available online: https://scholarworks.umass.edu/pare/vol23/iss1/1/ Guarcello, M. A., Levine, R. A., Beemer, J., Frazee, J. P., Laumakis, M. A., and Schellenberg, S. A. (2017). Balancing Student Success: Assessing Supplemental Instruction through Coarsened Exact Matching. Technology, Knowledge, and Learning 22, 335- 352. 2018 Outstanding SI Research Award by International Center for Supplemental Instruction. Spoon, K., Beemer, J., Whitmer, J. C., Fan, J., Frazee, J. P., Stronach, J., Bohonak, A. J., Levine, R. A. (2016). Random Forests for Evaluating Pedagogy and Informing Personalized Learning. Journal of Educational Data Mining 8, 20-50. Quarfoot, D. and Levine, R. A. (2016). How Reliable is Your Multi-Rater Inter-Rater
Reliability Index? The American Statistician 70, 373-384. Current and Recent Research Projects2016-2020, 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 AppointmentsFaculty Advisor, SDSU Analytic Studies and Institutional Research, 2015-present Associate Editor, Journal of Computational and Graphical Statistics, 2014-present At-Large Board Member, Interface Foundation of North America Board of Directors, 2014-present Member, Committee on Meetings, American Statistical Association, 2018-present Associate Editor for Statistics of the Notices of the AMS, 2019-2021. Human Interest questions
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