text: With large student enrollment, MOOC instructors face the unique challenge in deciding when to intervene in forum discussions with their limited bandwidth. We study this problem of instructor intervention. Using a large sample of forum data culled from 61 courses, we design a binary classifier to predict whether an instructor should intervene in a discussion thread or not. By incorporating novel information about a forum's type into the classification process, we improve significantly over the previous state-of-the-art. We show how difficult this decision problem is in the real world by validating against indicative human judgment, and empirically show the problem's sensitivity to instructors' intervention preferences. We conclude this paper with our take on the future research issues in intervention.
  @cite_18: Massive Open Online Courses (MOOCs) have experienced a recent boom in interest. Problems students struggle with in the discussion forums, such as difficultly in finding interesting discussion opportunities or attracting helpers to address posted problems, provide new opportunities for recommender systems. In contrast to traditional product recommendation, question recommendation in discussion forums should simultaneously consider constraints on both students and questions. These considerations include (1) Load Balancing - students should not be over-burdened with too many requests; and (2) Expertise Matching - students should not be requested to address problems they are not capable of addressing. In this work, we formulate a novel constrained question recommendation problem to address the above considerations. We design a context-aware matrix factorization model to predict students' preferences over questions, then build a max cost flow model to manage the constraints. Experimental results conducted on three MOOC datasets demonstrate that our method significantly outperforms baseline methods in optimizing overall forum welfare, and in predicting which specific questions students might be interested in.
  @cite_22: Online discussion forums are a valuable resource for people looking to find information, discuss ideas, and get advice on the Internet. Unfortunately, many forums have too much activity and information available, resulting in information overload. Moderation systems are implemented in some forums as a way to handle this problem, but due to sparsity issues, they are often not sufficient. We describe a novel method for learning from past moderations to develop a classifier that can perform automated moderation and thus address the sparsity problem. Additionally, we discuss the possibility of training a moderating classifier on a moderated forum and then applying it to an otherwise unmoderated forum.
  @cite_21: Discussion forums serve as a platform for student discussions in massive open online courses (MOOCs). Analyzing content in these forums can uncover useful information for improving student retention and help in initiating instructor intervention. In this work, we explore the use of topic models, particularly seeded topic models toward this goal. We demonstrate that features derived from topic analysis help in predicting student survival.
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  @cite_12: 'CCK08' was a unique event on Connectivism and Connective Knowledge within a MOOC (Massive Open Online Course) in 2008. It was a course and a network about the emergent practices and the theory of Connectivism, proposed by George Siemens as a new learning theory for a digital age. It was convened and led by Stephen Downes and George Siemens through the University of Manitoba, Canada. Although the event was not formally advertised, more than 2000 participants from all over the world registered for the course, with 24 of these enrolled for credit. The course presented a unique opportunity to discover more about how people learn in large open networks, which offer extensive diversity, connectivity and opportunities for sharing knowledge. Learners are increasingly exercising autonomy regarding where, when, how, what and with whom to learn. To do this, they often select technologies independent of those offered by traditional courses. In CCK08 this autonomy was encouraged and learning on the course was distributed across a variety of platforms. This paper explores the perspectives of some of the participants on their learning experiences in the course, in relation to the characteristics of connectivism outlined by Downes, i.e. autonomy, diversity, openness and connectedness interactivity. The findings are based on an online survey which was emailed to all active participants and email interview data from self-selected interviewees. The research found that autonomy, diversity, openness and connectedness interactivity are indeed characteristics of a MOOC, but that they present paradoxes which are difficult to resolve in an online course. The more autonomous, diverse and open the course, and the more connected the learners, the more the potential for their learning to be limited by the lack of structure, support and moderation normally associated with an online course, and the more they seek to engage in traditional groups as opposed to an open network. These responses constrain the possibility of having the positive experiences of autonomy, diversity, openness and connectedness interactivity normally expected of an online network. The research suggests that the question of whether a large open online network can be fused with a course has yet to be resolved. Further research studies with larger samples are needed, as is an investigation into the ethical considerations which may need to be taken into account when testing new theory and practice on course participants.
summary: Further, strategies for thread recommendation for students such as @cite_18 may not apply in recommending for instructors. This difference is partially due to scale: while the number of students and threads are large, there are few instructors per course. In this case, reliance on collaborative filtering using a user--item matrix is not effective. Learning from previous human moderation decisions @cite_22 , therefore, becomes the most feasible approach. Prior work on MOOC forums propose categorisation of posts @cite_21 @cite_0 @cite_2 to help instructors identify threads to intervene. Chaturvedi @cite_12 , the closest related work to ours, show each of the four states of their sequence models to predict instructor intervention to be distributed over four post categories they infer. In this paper, we use their results for comparison.