Understanding why students stopout will help in understanding how students learn in MOOCs. In this report, part of a 3 unit compendium, we describe how we build accurate predictive models of MOOC student stopout. We document a scalable, stopout prediction methodology, end to end, from raw source data to model analysis. We attempted to predict stopout for the Fall 2012 offering of 6.002x. This involved the meticulous and crowd-sourced engineering of over 25 predictive features extracted for thousands of students, the creation of temporal and non-temporal data representations for use in predictive modeling, the derivation of over 10 thousand models with a variety of state-of-the-art machine learning techniques and the analysis of feature importance by examining over 70000 models. We found that stop out prediction is a tractable problem. Our models achieved an AUC (receiver operating characteristic area-under-the-curve) as high as 0.95 (and generally 0.88) when predicting one week in advance. Even with more difficult prediction problems, such as predicting stop out at the end of the course with only one weeks' data, the models attained AUCs of 0.7.
MOOCs gather a rich array of click-stream information from students who interact with the platform. However, without student background information, inferences do not take advantage of a deeper understanding of students’ prior experiences, motivation, and home environment. In this poster, we investigate the predictive power of student background factors as well as student experiences with learning materials provided in the first MITx course, “Circuits and Electronics.” We focus on a group of survey completers who were given background questions, and we use multiple regression methods to investigate the relationship between achievement, online resource use, and student background. Online course providers may be able to better tailor online experiences to students when they know how background characteristics mediate the online experience. Abstract “Circuits and Electronics” (6.002x), which began in March 2012, was the ﬁrst MOOC developed by edX, the consortium led by MIT and Harvard. Over 155,000 students initially registered for 6.002x, which was composed of video lectures, interactive problems, online laboratories, and a discussion forum. As the course ended in June 2012, researchers began to analyze the rich sources of data it generated. This article describes both the ﬁrst stage of this research, which examined the students’ use of resources by time spent on each, and a second stage that is producing an in-depth picture of who the 6.002x students were, how their own background and capabilities related to their achievement and persistence, and how their interactions with 6.002x’s curricular and pedagogical components contributed to their level of success in the course. Studying Learning in the Worldwide ClassroomResearch into edX’s First MOOC F rom the launch of edX, the joint venture between MIT and Harvard to create and disseminate massive online open courses (MOOCs), the leaders of both institutions have emphasized that research into learning will be one of the initiative’s core missions. As numerous articles in both the academic and popular press have pointed out, the ability of MOOCs to generate a tremendous amount of data opens up considerable opportunities for educational research. edX and Coursera, which together claim almost four and a half million enrollees, have developed platforms that track students’ every click as they use instructional resources, complete assessments, and engage in social interactions. These data have the potential to help researchers identify, at a ﬁner resolution than ever before, what contributes to students’ learning and what hampers their success. The challenge for the research and assessment communities is to determine which questions should be asked and in what priority. How can we set ourselves on a path that will produce useful short-term results while providing a foundation upon which to build? What is economically feasible? What is politically possible? How can research into MOOCs contribute to an understanding of on-campus learning? What do stakeholders—faculty, developers, government agencies, foundations, and, most importantly, students—need in order to realize the potential of digital learning, generally, and massive open online courses, speciﬁcally? In massive open online courses (MOOCs), low barriers to registration attract large numbers of students with diverse interests and backgrounds, and student use of course content is asynchronous and unconstrained. The authors argue that MOOC data are not only plentiful and different in kind but require reconceptualization—new educational variables or different interpretations of existing variables. The authors illustrate this by demonstrating the inadequacy or insufficiency of conventional interpretations of four variables for quantitative analysis and reporting: enrollment, participation, curriculum, and achievement. Drawing from 230 million clicks from 154,763 registrants for a prototypical MOOC offering in 2012, the authors present new approaches to describing and understanding user behavior in this emerging educational context. Massive open online courses (MOOCs) provide learning materials and automated assessments for large numbers of virtual users. Because every interaction is recorded, we can longitudinally model performance over the course of the class. We create a panel model of achievement in an early MOOC to estimate within- and between-user differences. In this study, we hope to contribute to HCI literature by, first, applying quasi-experimental methods to identify behaviors that may support student learning in a virtual environment, and, second, by using a panel model that takes into account the longitudinal, dynamic nature of a multiple-week class.