Evaluating Predictive Models of Student Success: Closing the Methodological Gap

Dropout Model Evaluation in MOOCs

The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we present a procedure to statistically test hypotheses about model performance which goes beyond the state-of-the-practice in the community to analyze both algorithms and feature extraction methods from raw data. We apply thi...

Student Success Prediction in MOOCs

Predictive models of student success in Massive Open Online Courses (MOOCs) are a critical component of effective content personalization and adaptive interventions. In this article we review the state of the art in predictive models of student success in MOOCs and present a dual categorization of MOOC research according to both predictors (features) and prediction (outcomes). We critically sur...

A Framework for Reducing the College Success Gap and Promoting Success for All

Policymakers, practitioners, and scholars have directed tremendous attention to the goal of improving “student success.” Based on a review of largely discrete existing bodies of literature, this report proposes an overarching framework that policymakers, practitioners, and researchers can use to develop, implement, and evaluate policies and practices for addressing persistent racial/ethnic and ...

PREDICTIVE ANALYTICS FOR STUDENT SUCCESS: Developing Data-Driven Predictive Models of Student Success

An Analysis of a Need-based Student Aid Program for Georgia

There is a large gap in college enrollment by family income, and there is evidence that this gap is growing. Yet the benefits of a college education, both for the individual and society, are significant. Encouraging college education in general and closing that income gap in enrollment is a long standing policy objective for the United States and Georgia. One policy aimed at closing the income ...