This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video clickstream interactions and forum activities, we seek to fundamentally understand traits that are predictive of decreasing engagement over time. Grounded in the interdisciplinary field of network science, we follow a graph based approach to successfully extract indicators of active and passive MOOC participation that reflect persistence and regularity in the overall interaction footprint. Using these rich educational semantics, we focus on the problem of predicting student attrition, one of the major highlights of MOOC literature in the recent years. Our results indicate an improvement over a baseline ngram based approach in capturing "attrition intensifying" features from the learning activities that MOOC learners engage in. Implications for some compelling future research are discussed.
In this section we outline perspectives on student attrition that have been explored so far in the literature on MOOCs. Much of this work successfully leverages effective feature engineering and advanced statistical methods. However, the biggest limitation of most of these emerging works is that they focus solely on discussion forum behavior or video lecture activity, but do not fuse and take them into account. Some of these works have grown out of research on predicting academic progress of students and identifying students those who are at dropout risk @cite_9 @cite_2 @cite_5 @cite_7 @cite_14 .
@cite_14 @cite_7 @cite_9 @cite_2 @cite_5
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High attrition rates are one of the biggest concerns in MOOCs. One of the possible causes may be learners’ lack of interactions and low levels of participations in MOOCs online discussions. Research to measure and predict recurrent interactions of learners in MOOCs online discussions has the potential to gain inside into the likely impact on the attrition rate. It is argued that personalisation in MOOCs has the potential to increase learners’ interactions and associated factors to continuous friendships. In this paper, a detailed analysis has been carried out of learners’ interactions within a MOOC. This paper investigates learners’ interaction habits and their recurrent interactions throughout the entire duration of a MOOC’s course, and consequently proposes a method to measure the interactions and predict possible interactions between peers. The findings denote that when a learner interacted with their peer, they most probably interact again in the following weeks. Moreover, our proposed prediction method also demonstrate promising results towards predicting future interactions between learners based on their previous relationships In this paper, we explore student dropout behavior in a Massively Open Online Course (MOOC). We use a survival model to measure the impact of three social factors that make predictions about attrition along the way for students who have participated in the course discussion forum. We examine the process of engineering features for developing models that improve our understanding of learners' online behavior in MOOCs. Because feature engineering relies so heavily on human insight, we argue that extra effort should be made to engage the crowd for feature proposals and even their operationalization. We show two approaches where we have started to engage the crowd. We also show how features can be evaluated for their relevance in predictive accuracy. When we examined crowd-sourced features in the context of predicting stopout, not only were they nuanced, but they also considered more than one interaction mode between the learner and platform and how the learner was relatively performing. We were able to identify different influential features for stop out prediction that depended on whether a learner was in 1 of 4 cohorts defined by their level of engagement with the course discussion forum or wiki. This report is part of a compendium which considers different aspects of MOOC data science and stop out prediction. 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.