We study user behavior in the courses offered by a major Massive Online Open Course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable education in MOOCs and is done via online discussion forums, our main focus is in understanding forum activities. Two salient features of MOOC forum activities drive our research: 1. High decline rate: for all courses studied, the volume of discussions in the forum declines continuously throughout the duration of the course. 2. High-volume, noisy discussions: at least 30% of the courses produce new discussion threads at rates that are infeasible for students or teaching staff to read through. Furthermore, a substantial portion of the discussions are not directly course-related.
We investigate factors that correlate with the decline of activity in the online discussion forums and find effective strategies to classify threads and rank their relevance. Specifically, we use linear regression models to analyze the time series of the count data for the forum activities and make a number of observations, e.g., the teaching staff's active participation in the discussion increases the discussion volume but does not slow down the decline rate. We then propose a unified generative model for the discussion threads, which allows us both to choose efficient thread classifiers and design an effective algorithm for ranking thread relevance. Our ranking algorithm is further compared against two baseline algorithms, using human evaluation from Amazon Mechanical Turk.
The authors on this paper are listed in alphabetical order. For media and press coverage, please refer to us collectively, as "researchers from the EDGE Lab at Princeton University, together with collaborators at Boston University and Microsoft Corporation."
From semantic memory to collective creativity: A generative cognitive foundation for social creativity models
Khoury College of Computer Sciences, Northeastern University（东北大学科里计算机学院）；Bellini College of Artificial Intelligence, Cybersecurity and Computing, University of South Florida（南佛罗里达大学贝利尼人工智能、网络安全与计算学院）
Designed to Spread: A Generative Approach to Enhance Information Diffusion
Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous System, Tongji University（同济大学上海智能自主系统研究院）；Shanghai Innovation Institute, Shanghai, China（上海创新研究院）
Survey of Generative Methods for Social Media Analysis
Faculty of Computer Science, Dalhousie University（达尔豪斯大学计算机科学学院）；Institute of Computer Science, Polish Academy of Sciences（波兰科学院计算机科学研究所）；Department of Mathematics, Ryerson University（瑞尔森大学数学系）；Department of Computer Science, Memorial University of Newfoundland（纽芬兰纪念大学计算机科学系）；Tutte Institute for Mathematics and Computing（塔特数学与计算研究所）
Exploring the Use of Abusive Generative AI Models on Civitai
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Queen Mary University of London（伦敦大学玛丽女王学院）；University of Helsinki（赫尔辛基大学）
