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.
While the question of necessity of instructor's intervention in online learning and MOOCs is being investigated @cite_3 @cite_4 , technologies to enable timely and appropriate intervention are also required. The pedagogy community has recognized the importance of instructor intervention in online learning prior to the MOOC era (e.g., @cite_10 ). Taking into consideration the pedagogical rationale for effective intervention, they also proposed strategic instructor postings: to guide discussions, to wrap-up the discussion by responding to unanswered questions, with Socrates-style'' follow-up questions to stimulate further discussions, or with a mixture of questions and answers @cite_14 . However, these strategies must be revisited when being applied to the scale of typical MOOC class sizes.
@cite_10 @cite_14 @cite_4 @cite_3
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This research investigates the impact professors, and other instructional staff, have on student content knowledge acquisition in a physical science MOOC offered through the University of Illinois at Urbana-Champaign. An A B test was used to randomly assign MOOC participants in either a control group (with no instructional interaction) or an intervention group (in which the professor and teaching assistants responded to comments in the discussion and complied summary weekly feedback statements) to identify the differences in learning outcomes, participation rates, and student satisfaction. The study found that instructor intervention had no statistically significant impact on overall completion rates, overall badge acquisition rates, student participation rates, or satisfaction with the course, but did (p The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood. In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high- and low-achieving students, and investigate how forum participation relates to other parts of the course. We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations. We find that making badges more salient produced increases in forum engagement. 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) 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.