In this paper we examine how different types of participants in a physics Massive Open Online Course (MOOC) tend to use the existing course resources. We use data from the 2013 offering of the Massive Open Online Course 8.MReVx designed by the RELATE (REsearch in Learning Assessing and Tutoring Effectively) Group at the Massachusetts Institute of…

The present study examined undergraduate students' multiple source use in response to two different types of academic questions, one discrete and one open-ended. Participants (N = 240) responded to two questions using a library of eight digital sources, varying in source type (e.g., newspaper article) and reliability (e.g., authors' credentials).…

This study focused on the academic performance and use of disability support services (DSS) of students with different types of disabilities who attend a postsecondary education institution. Findings show different patterns of academic success over four semesters as well as different patterns of DSS usage. Students who requested support from DSS…

The purpose of this study was to investigate the relationship between academic self-efficacy and preferences regarding the use of text materials and in-class activities of college students at a university that serves one of the highest-poverty regions in the United States. A convenient cluster sample of 105 students taking summer classes at a…

A segmentation study is used to partition college students into groups that are more or less likely to adopt tablet technology as a learning tool. Because the college population chosen for study presently relies upon laptop computers as their primary learning device, tablet technology represents a "next step" in technology. Student…

This research examined teachers' online behaviors while using a digital library service--the Instructional Architect (IA)--through three consecutive studies. In the first two studies, a statistical model called latent class analysis (LCA) was applied to cluster different groups of IA teachers according to their diverse online behaviors. The third…

In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…