This study investigates stopout patterns in MOOCs to understand course and assessment-level factors that influence student stopout behavior. We expanded previous work on stopout by assessing the exponential decay of assessment-level stopout rates across courses. Results confirm a disproportionate stopout rate on the first graded assessment. We then evaluated which course and assessment level features were associated with stopout on the first assessment. Findings suggest that a higher number of questions and estimated time commitment in the early assessments and more assessments in a course may be associated with a higher proportion of early stopout behavior.

Education research has explored the role of students' affective states in learning, but some evidence suggests that existing models may not fully capture the meaning or frequency of how students transition between different states. In this study we examine the patterns of educationally-relevant affective states within the context of Betty's Brain, an open-ended, computer-based learning system used to teach complex scientific processes. We examine three types of affective transitions based on similarity with the theorized D'Mello and Graesser model, transition between two affective states, and the sustained instances of certain states. We correlate of the frequency of these patterns with learning outcomes and our findings suggest that boredom is a powerful indicator of students' knowledge, but not necessarily indicative of learning. We discuss our findings within the context of both research and theory on affect dynamics and the implications for pedagogical and system design.

This paper examines the effect of different linguistic features (as identified through Natural Language Processing tools) on affective measures of student engagement using a discovery with models approach. We build on previous literature, using automated detectors that identify when a middle-school student using an online mathematics tutor is experiencing boredom, confusion, frustration, or engaged concentration, to identify which problems are most engaging (or not) at scale. We then apply previously validated NLP tools to determine the degree to which engagement findings may be related to the linguistic properties of word problems, contributing to a growing literature on the effects of language on mathematics learning.

Advances in the learning analytics community have created opportunities to deliver early warnings that alert teachers and instructors when a student is at risk of not meeting academic goals [6], [71]. Alert systems have also been developed for school district leaders [33] and for academic advisors in higher education [39], but other professionals in the K-12 system, namely guidance counselors, have not been widely served by these systems. In this study, we use college enrollment models created for the ASSISTments learning system [55] to develop reports that target the needs of these professionals, who often work directly with students, but usually not in classroom settings. These reports are designed to facilitate guidance counselors' efforts to help students to set long term academic and career goals. As such, they provide the calculated likelihood that a student will attend college (the ASSISTments College Prediction Model or ACPM), alongside student engagement and learning measures. Using design principles from risk communication research and student feedback theories to inform a co-design process, we developed reports that can inform guidance counselor efforts to support student achievement.

Student knowledge modeling is an important part of modern personalized learning systems, but typically relies upon valid models of the structure of the content and skill in a domain. These models are often developed through expert tagging of skills to items. However, content creators in crowdsourced personalized learning systems often lack the time (and sometimes the domain knowledge) to tag skills themselves. Fully automated approaches that rely on the covariance of correctness on items can lead to effective skill-item mappings, but the resultant mappings are often difficult to interpret. In this paper we propose an alternate approach to automatically labeling skills in a crowdsourced personalized learning system using correlated topic modeling, a natural language processing approach, to analyze the linguistic content of mathematics problems. We find a range of potentially meaningful and useful topics within the context of the ASSISTments system for mathematics problem-solving.