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 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. 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 lea We study student performance prediction in Massive Open Online Courses (MOOCs), where the objective is to predict whether a user will be Correct on First Attempt (CFA) in answering a question. In doing so, we develop novel techniques that leverage behavioral data collected by MOOC platforms. Using video-watching clickstream data from one of our MOOCs, we first extract summary quantities (e.g., fraction played, number of pauses) for each user-video pair, and show how certain intervals sets of values for these behaviors quantify that a pair is more likely to be CFA or not for the corresponding question. Motivated by these findings, our methods are designed to determine suitable intervals from training data and to use the corresponding success estimates as learning features in prediction algorithms. Tested against a large set of empirical data, we find that our schemes outperform standard algorithms (i.e., without behavioral data) for all datasets and metrics tested. Moreover, the improvement is particularly pronounced when considering the first few course weeks, demonstrating the early detection capability of such clickstream data. We also discuss how CFA prediction can be used to depict graphs of the Social Learning Network (SLN) of students, which can help instructors manage courses more effectively.