The Statistical Modeling Group (SMG) is a unit within Analytic Studies & Institutional Research (ASIR) that utilizes a variety of statistical and predictive modeling frameworks to conduct research around high priority campus initiatives related to student success and the strategic priorities of the campus. SMG focuses on actionable outcomes and innovative solutions with predictive and prescriptive analytics that help drive student success at SDSU.
Recently published SMG research
- Random forest of interaction trees for estimating individualized treatment regimes with ordered treatment levels in observational studies. Journal of Data Science.
- A Learning Analytics Case Study: On Class Sizes in Undergraduate Writing Courses. Stat.
- On machine learning methods for propensity score matching and weighting in educational data mining applications. Artificial Intelligence in STEM Education.
- Machine Learning Methods for Course Enrollment Prediction. Strategic Enrollment Management Quarterly.
- A Combinatorial Optimization Framework for Scoring Students in University Admissions. Evaluation Review
- Estimating a Dose-Response Relationship in Quasi- Experimental Student Success Studies. International Journal of Artificial Intelligence in Education.
- Estimating the Optimal Treatment Regime for Student Success Programs. Behaviormetrika.
- Causal inference in the presence of missing data using a random forest based matching algorithm. Stat.
- Stacked Ensemble Learning for Propensity Score Methods in Observational Studies. Journal of Educational Data Mining.
- Using a Latent Class Forest to Identify At-Risk Students in Higher Education. Journal of Educational Data Mining.
- Predictive Analytics Machinery for STEM Student Success Studies. Applied Artificial Intelligence.
- Assessing Instructional Modalities: Individualized Treatment Effects for Personalized Learning. Journal of Statistics Education.
- Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research. Practical Assessment, Research, and Evaluation.
- Random Forests for Evaluating Pedagogy and Informing Personalized Learning. Journal of Educational Data Mining.
