Data Envelopment Analysis (DEA) is a widely adopted non-parametric technique for evaluating R&D performance. However, traditional DEA models often struggle to provide reliable solutions in the presence of data uncertainty. To address this limitation, this study develops a novel robust super-efficiency DEA approach to evaluate R&D…

In educational technology and learning sciences, there are multiple uses for a predictive model of whether a student will perform a task correctly or not. For example, an intelligent tutoring system may use such a model to estimate whether or not a student has mastered a skill. We analyze the significance of data recency in making such…

Providing remedial (also known as developmental) education is the primary way colleges cope with students who do not have the academic preparation needed to succeed in college-level courses. Remediation is widespread, with nearly one-third of entering freshman taking remedial courses at a cost of at least $1 billion per year. Despite its…

This book discusses how to use everyday data to create strategies for educational improvement in schools. It provides users with a process for building a performance indicator system, identifying the most important aspects of a school's innovational efforts and traps to avoid that may lead to misinterpretation of data. The introduction identifies…