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_5: Online discussion forums are a valuable means for users to resolve specific information needs, both interactively for the participants and statically for users who search browse over historical thread data. However, the complex structure of forum threads can make it difficult for users to extract relevant information. Automatically identifying whether the problem in a thread has been solved or not can help direct users to threads where the original problem has been solved, hence enhancing their prospects of solving their particular problem. In this paper, we investigate the task of Solvedness classification by exploiting the discourse structure of forum threads. Experimental results show that simple features derived from thread discourse structure can greatly boost the accuracy of Solvedness classification, which has been shown to be very difficult in previous research.
  @cite_16: Discussion threads form a central part of the experience on many Web sites, including social networking sites such as Facebook and Google Plus and knowledge creation sites such as Wikipedia. To help users manage the challenge of allocating their attention among the discussions that are relevant to them, there has been a growing need for the algorithmic curation of on-line conversations --- the development of automated methods to select a subset of discussions to present to a user. Here we consider two key sub-problems inherent in conversational curation: length prediction --- predicting the number of comments a discussion thread will receive --- and the novel task of re-entry prediction --- predicting whether a user who has participated in a thread will later contribute another comment to it. The first of these sub-problems arises in estimating how interesting a thread is, in the sense of generating a lot of conversation; the second can help determine whether users should be kept notified of the progress of a thread to which they have already contributed. We develop and evaluate a range of approaches for these tasks, based on an analysis of the network structure and arrival pattern among the participants, as well as a novel dichotomy in the structure of long threads. We find that for both tasks, learning-based approaches using these sources of information yield improvements for all the performance metrics we used.
  @cite_7: Automatic tools for analyzing student online discussions are highly desirable for providing better assistance and promoting discussion participation. This paper presents an approach for identifying student discussions with unresolved issues or unanswered questions. In order to handle highly incoherent data, we perform several data processing steps. We then apply a two-phase classification algorithm. First, we classify "speech acts" of individual messages to identify the roles that the messages play, such as question, issue raising, and answers. We then use the resulting speech acts as features for classifying discussion threads with unanswered questions or unresolved issues. We performed a preliminary analysis of the classifiers and the system shows an average F score of 0.76 in discussion thread classification.
  @cite_11: Microblogging networks serve as vehicles for reaching and influencing users. Predicting whether a message will elicit a user response opens the possibility of maximizing the virality, reach and effectiveness of messages and ad campaigns on these networks. We propose a discriminative model for predicting the likelihood of a response or a retweet on the Twitter network. The approach uses features derived from various sources, such as the language used in the tweet, the user's social network and history. The feature design process leverages aggregate statistics over the entire social network to balance sparsity and informativeness. We use real-world tweets to train models and empirically show that they are capable of generating accurate predictions for a large number of tweets.
summary: Among works on forum information retrieval, we focus on those that focus on forum moderation as their purpose is similar to the instructor's role in a course forum. While early work focused on automated spam filtering, recent works shifted focus towards curating large volumes of posts on social media platforms @cite_16 to distil the relevant few. Specifically, those that strive to identify thread solvedness @cite_5 @cite_7 and completeness @cite_11 are similar to our problem.