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.
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.
@cite_18 @cite_22 @cite_21 @cite_0 @cite_2 @cite_12
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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 lea When facilitating asynchronous discussion forums, should online instructors be encouraged to take a prominent 'sage on the stage' role, a more constructivist 'guide on the side' role, or an ultra low profile as 'the ghost in the wings'? There is no shortage of anecdotal advice on how to conduct discussion forums in online education, but there appears to be very little research available so far to back that advice up. In this study of an online astronomy program with approximately 200 participants, we investigated the way that the rate at which instructors post and how often those instructors initiate discussions correlate with several variables--student posting rates, lengths of discussion threads, and student survey responses concerning their educational experience. We found that the ways in which instructors post to forums can influence students' forum discussions and perceptions, but not always in expected ways. On average, frequent posting by instructors did not lead to more student postings, and the more the instructors posted, the shorter were the lengths of the discussions overall. On the other hand, while most students rated their educational experience highly, instructors who posted frequently were judged on average to be more enthusiastic and expert than those who did not. Clearly the number of student postings and the rate at which instructors participate are not simple indicators of the quality of forum discussions. We need to find more subtle measures of the effectiveness of asynchronous discussion forums for learning and teaching. 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. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. 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 Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance.