Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…

This study developed a learning system that allows teachers to edit assignments designed to teach students the text structure strategy through the use of four phases: instructing, modeling, practicing, and reflecting. A 7-week instructional experiment was conducted in which 84 12th-grade students learned the text structure strategy using this…

Learning assistance is an essential part of higher education. Tutors, the core of the assistance staff, need to have learning assistance skills. If the potential for these skills can be identified when selecting tutors, the training method will be more efficient. Also, learning assistance skills are thought to be related to learning skills.…

Understanding the factors that influence students' results in hybrid learning environments is becoming increasingly important in today's educational environment. The goal of this research is to examine factors that influence students' academic performance as well as their level of participation in blended learning environments. A comprehensive…

This chapter reviews actionable data use--both as an umbrella term and as a specific concept--developed in three different traditions that data/information can inform and guide P-20 educational practice toward better outcomes. The literatures reviewed are known as data-driven decision making (DDDM), education data mining (EDM), and learning…