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.
Some prior research has focused on deriving social positioning metrics within discussion forums to understand influencing factors that lead to differently motivated behaviors of students. For example, @cite_8 @cite_1 used aggregate post-reply discussion forum graph per week, with an aim to investigate posting behavior and collaborative aspects of participation through operationalizations of social positioning. However, we work at a much finer granularity in the current study and our focus is on individual student modeling instead. We capture not only forum participation trajectory, but also video lecture viewing activity of every student in their participation week. Modeling the combined interaction footprint as an activity network, allows us to decipher the type of engagement and organization of behavior for each student, which are reflective of attrition.
@cite_1 @cite_8
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As asynchronous discussion forums become more prevalent in online and flexible-delivery modes of teaching, understanding the role that instructors play in student learning in these forums becomes an important issue. Whether the instructor chooses to lead discussions or to keep a low profile can affect student participation in surprising ways. In this study, we investigate how instructor participation rates, the timing of instructor postings (during or at the end of a forum) and nature of their postings (questions, answers or a mix of the two) relate to student participation and perception. Using archives containing over 40,000 postings to nearly 400 discussion forums, together with over 500 university evaluation survey responses collected over six consecutive semesters, we analysed student and instructor postings to discussion forums, student responses to university evaluation surveys, and instructor and student responses to surveys carried out as part of this project. We collected both quantitative and qualitative responses on how instructors believe they behave in discussion forums and compared these with statistical analysis of the forums, and then looked at how the instructors' behaviour correlated with students' participation and perceptions. We found that the way that instructors post to forums may influence students' forum discussions and participation in unexpected ways. We show that instructors' perceptions of how they teach online do not necessarily agree with our observations, and that intuitive measures such as the rate of student participation and the length of discussion threads are not necessarily good ways to judge the 'health' of discussion forums or the quality of learning taking place. Previous studies have established the link between one's actions (e.g., engaging with others vs. minding one's own business) and one's national culture (e.g., collectivist vs. individualistic), and such actions have been shown to be important as they are collectively affiliated with a country's economic outcomes (e.g., Gross Domestic Product). Hitherto there has not been any systematic study of whether one's action on Twitter (e.g., deciding when to post messages) is linked to one's culture (e.g., country's Pace of Life). To fix that, we build different network snapshots starting from 55,000 seed users on Twitter, and we do so for 10 weeks across 30 countries (after filtering those with low penetration rates) for a total of 2.34 M profiles. Based on Hofstede's theory of cultural dimensions and Levine's Pace of Life theory, we consider three behavioral patterns on Twitter (i.e., temporal predictability of tweets, engaging with others, and supporting others who are less popular) and associate them with three different dimensions derived from the two theories: Pace of Life, Individualism and Power Distance. We find the following strong correlations: activity predictability negatively correlates with Pace of Life (r=-0.62), tweets with mentions negatively correlates with Individualism (r = -0.55), and power (e.g, Twitter popularity) imbalance in relationships (between, for example, two users mentioning each other) is correlated with Power Distance (r=0.62). These three cultural dimensions matter because they are associated with a country's socio-economic aspects - with GDP per capita, income inequality, and education expenditure. While our daily activities usually involve interactions with others, the current methods on activity recognition do not often exploit the relationship between social interactions and human activity. This paper addresses the problem of interpreting social activity from human interactions captured by mobile sensing networks. Our first goal is to discover different social activities such as chatting with friends from interaction logs and then characterize them by the set of people involved, and the time and location of the occurring event. Our second goal is to perform automatic labeling of the discovered activities using predefined semantic labels such as coffee breaks, weekly meetings, or random discussions. Our analysis was conducted on a real-life interaction network sensed with Bluetooth and infrared sensors of about fifty subjects who carried sociometric badges over 6 weeks. We show that the proposed system reliably recognized coffee breaks with 99 accuracy, while weekly meetings were recognized with 88 accuracy. In this paper, we propose a methodology to predict the popularity of online contents. More precisely, rather than trying to infer the popularity of a content itself, we infer the likelihood that a content will be popular. Our approach is rooted in survival analysis where predicting the precise lifetime of an individual is very hard and almost impossible but predicting the likelihood of one's survival longer than a threshold or another individual is possible. We position ourselves in the standpoint of an external observer who has to infer the popularity of a content only using publicly observable metrics, such as the lifetime of a thread, the number of comments, and the number of views. Our goal is to infer these observable metrics, using a set of explanatory factors, such as the number of comments and the number of links in the first hours after the content publication, which are observable by the external observer. We use a Cox proportional hazard regression model that divides the distribution function of the observable popularity metric into two components: a) one that can be explained by the given set of explanatory factors (called risk factors) and b) a baseline distribution function that integrates all the factors not taken into account. To validate our proposed approach, we use data sets from two different online discussion forums: dpreview.com, one of the largest online discussion groups providing news and discussion forums about all kinds of digital cameras, and myspace.com, one of the representative online social networking services. On these two data sets we model two different popularity metrics, the lifetime of threads and the number of comments, and show that our approach can predict the lifetime of threads from Dpreview (Myspace) by observing a thread during the first 5 6 days (24 hours, respectively) and the number of comments of Dpreview threads by observing a thread during first 2 3 days.