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.
Within the use of behavioral data, the most common behavioral variables used are performance related either prior to or during the course. For example, @cite_44 use prior academic performance (education level), other even use high school GPA, college GPA, freshmen year GPA @cite_24 @cite_11 . Some studies compose variables based on project grade and test grades during the course @cite_44 . In almost all cases, prior academic performance has been found to be the highest predictor of the student persistence @cite_11 @cite_50 @cite_34 .
@cite_44 @cite_24 @cite_50 @cite_34 @cite_11
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In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to accurately classify some e-learning students, whereas another may succeed, three decision schemes, which combine in different ways the results of the three machine learning techniques, were also tested. The method was examined in terms of overall accuracy, sensitivity and precision and its results were found to be significantly better than those reported in relevant literature. A classification rule was developed to predict undergraduate students& withdrawal from or completion of fully online general education courses. A multivariate technique, predictive discriminant analysis (PDA), was used. High school grade point average and SAT mathematics score were shown to be related to retention in the online university courses. Locus of control and financial aid were able to identify dropout and completion with 74.5 accuracy. Abstract This paper focuses on university-level education offered by methods of distance learning in the field of computers and aims at the investigation of the main causes for student dropouts. The presented study is based on the students of the Course of “Informatics”, Faculty of Science and Technology of the Hellenic Open University and investigates the particularities of education provided through the use of computers and technology in general. This paper presents information about the students' profile, the use of computer technology, the percentage of dropouts, as well as a classification of the reasons for dropouts based on interviews with the students. The study shows that dropouts are correlated with the use of technological means and, based on this fact, the Hellenic Open University implemented interventions in the use of such means. It also proves that a correlation exists between dropouts and students' age, but not gender, although female students are more reluctant to start following a course. However, it is also shown that female students' commitment to a course is stronger and thus, they do not drop out as easily as male students do. Furthermore, the results of this study strongly correlate dropouts to the existence of previous education in the field of Informatics or to working with computers, but not to the degree of specialisation in computers. Finally, the paper presents the reasons provided by the students for drooping out, with the main reasons being the inability to estimate the time required for university-level studies and the perceived difficulty of the computers course. We hypothesized that college major persistence would be predicted by first-year academic performance and an interest-major composite score that is derived from a student’s entering major and two work task scores. Using a large data set representing 25 four-year institutions and nearly 50,000 students, we randomly split the sample into an estimation sample and a validation sample. Using the estimation sample, we found major-specific coefficients corresponding to the two work task scores that optimized the prediction of major persistence. Then, we applied the estimated coefficients to the validation sample to form an interest-major composite score representing the likelihood of persisting in entering major. Using the validation sample, we then tested a theoretical model for major persistence that incorporated academic preparation, the interest-major composite score, and first-year academic performance. The results suggest that (1) interest-major fit and first-year academic performance work to independently predict whether a student will stay in their entering major and (2) the relative importance of two work task scores in predicting major persistence depends on the entering major. The results support Holland’s theory of person-environment fit and suggest that academic performance and interest-major fit are key constructs for understanding major persistence behavior. Many students who start college intending to major in science or engineering do not graduate, or decide to switch to a non-science major. We used the recently developed statistical method of random forests to obtain a new perspective of variables that are associated with persistence to a science or engineering degree. We describe classification trees and random forests and contrast the results from these methods with results from the more commonly used method of logistic regression. Among the variables available in Arizona State University data, high school and freshman year GPAs have highest importance for predicting persistence; other variables such as number of science and engineering courses taken freshman year are important for subgroups of the student population. The method used in this study could be employed in other settings to identify faculty practices, teaching methods, and other factors that are associated with high persistence to a degree.