Supervised machine learning has become one of the most important methods for developing educational and intelligent tutoring software; it is the backbone of many educational data mining methods for estimating knowledge, emotion, and other aspects of learning. Hence, in order to ensure optimal utilization of computing resources and effective…

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…

Collaborative problem solving behaviors are difficult to identify and foster due to their amorphous and dynamic nature. In this paper, we investigate the value of considering early class period behaviors, based on small group development theory, for building predictive machine learning models of collaborative behaviors during problem solving. Over…

Metacognition is a valuable tool for learning, since it is closely related to self-regulation and awareness of one's own affect. However, methods for automatically detecting and studying metacognition are scarce. Thus, in this paper we describe an algorithm for automatic detection of metacognitive language in writing. We analyzed text from the…

Little is known about the online learning behaviors of students traditionally underrepresented in STEM fields (i.e., UR-STEM students), as well as how those behaviors impact important learning outcomes. The present study examined the relationship between online discussion forum engagement and success for UR-STEM and non-UR-STEM students, using the…