This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…

This document contains a design for creating an educational data bank of pupil personnel information. The specific information that should be collected and maintained in such an updatable data bank is described in detail. The document provides instructions on interpretation of this data for (1) predicting approximate percentages of students who…

Student and institutional characteristics related to college freshman persistence were studied. Persistence was examined for 5 institutions (5,950 students) at 4 points in time: end of first term, reenrollment in the spring, end of spring term, and reenrollment in the fall of the sophomore year. Data from a variety of sources were used, and…