@article {358, title = {Understanding Hybrid-MOOC Effectiveness with a Collective Socio-Behavioral Model}, journal = {Journal of Educational Data Mining (JEDM)}, volume = {11}, year = {2019}, pages = {42--77}, abstract = {Online courses for high school students promise the opportunity to bring critical education to youth most at need, bridging gaps which may exist in brick-and-mortar institutions. In this work, we investigate a hybrid Massive Open Online Course for high schoolers which includes an in-person coaching component. We address the efficacy of these courses and the contribution of in-person coaching. We first analyze features of student behavior and their effect on post-test performance and then propose a novel probabilistic model for inferring student success on an AP exam post-test. Our proposed model exploits relationships between students to collectively infer student success. When these relationships are not directly observed, we formulate latent constructs to capture social dynamics of learning. By collectively inferring student success as a function of both unobserved individual characteristics and relational dynamics, we improve predictive performance by up to 6.8\% over an SVM model with only observable features. We propose this general socio-behavioral modeling framework as a flexible approach for including unobserved aspects of learning in meaningful ways, in order to better understand and infer student success.}, doi = {10.5281/zenodo.3594773}, url = {https://doi.org/10.5281/zenodo.3594773}, author = {Sabina Tomkins and Lise Getoor} } @conference {339, title = {Understanding Evolution of Long-running MOOCs}, booktitle = {International Conference on Web Information Systems Engineering (WISE)}, year = {2018}, author = {Arti Ramesh and Lise Getoor} } @conference {kouki:recsys17, title = {User Preferences for Hybrid Explanations}, booktitle = {11th ACM Conference on Recommender Systems (RecSys)}, year = {2017}, author = {Kouki, Pigi and Schaffer, James and Pujara, Jay and ODonovan, John and Lise Getoor} } @conference {sridhar:akbc17, title = {Using Noisy Extractions to Discover Causal Knowledge}, booktitle = {NIPS Workshop on Automated Knowledge Base Construction}, year = {2017}, author = {Dhanya Sridhar and Pujara, Jay and Lise Getoor} } @conference {kumar:asonam16, title = {Unsupervised Models for Predicting Strategic Relations between Organizations}, booktitle = {ASONAM}, year = {2016}, abstract = {

Microblogging sites like Twitter provide a platform for sharing ideas and expressing opinions. The widespread popularity of these platforms and the complex social structure that arises within these communities provides a unique opportunity to understand the interactions between users. The political domain, especially in a multi-party system, presents compelling challenges, as political parties have different levels of alignment based on their political strategies. We use Twitter to understand the nuanced relationships between differing political entities in Latin America. Our model incorporates diverse signals from the content of tweets and social context from retweets, mentions and hashtag usage. Since direct communications between entities are relatively rare, we explore models based on the posts of users who interact with multiple political organizations. We present a quantitative and qualitative analysis of the results of models using different features, and demonstrate that a model capable of using sentiment strength, social context, and issue alignment has superior performance to less sophisticated baselines.

}, author = {Shachi Kumar and Jay Pujara and Lise Getoor and David Mares and Dipak Gupta and Ellen Riloff} } @conference {ramesh:nipsws15, title = {Understanding Influence in Online Professional Networks}, booktitle = {NIPS Workshop on Networks in Social and Information Sciences}, year = {2015}, keywords = {HL-MRFs, influence, professional networks, social networks}, author = {Ramesh, Arti and Rodriguez, Mario and Lise Getoor} } @conference {bach:aistats15, title = {Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees}, booktitle = {Artificial Intelligence and Statistics (AISTATS)}, year = {2015}, author = {Bach, Stephen H. and Huang, Bert and Lise Getoor} } @article {pujara:aimag15, title = {Using Semantics \& Statistics to Turn Data into Knowledge}, journal = {AI Magazine}, volume = {36}, number = {1}, year = {2015}, pages = {65{\textendash}74}, author = {Pujara, Jay and Miao, Hui and Lise Getoor and Cohen, William} } @conference {ramesh:las13, title = {Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs}, booktitle = {ACM Conference on Learning at Scale}, series = {Annual Conference Series}, year = {2014}, publisher = {ACM}, organization = {ACM}, keywords = {MOOC, learner engagement, learning analytics, online education, probabilistic modeling, structured prediction}, author = {Ramesh, Arti and Goldwasser, Dan and Huang, Bert and Daume III, Hal and Lise Getoor} } @conference {ramesh:aclws14, title = {Understanding MOOC Discussion Forums using Seeded LDA}, booktitle = {ACL Workshop on Innovative Use of NLP for Building Educational Applications}, year = {2014}, publisher = {ACL}, organization = {ACL}, abstract = {

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.

}, keywords = {LDA, MOOC Discussion Forums, Seeded LDA, structured prediction}, author = {Arti Ramesh and Dan Goldwasser and Bert Huang and Hal Daume III and Lise Getoor} } @conference {ramesh:nips12, title = {User Role Prediction in Online Discussion Forums using Probabilistic Soft Logic}, booktitle = {NeuRIPS Workshop on PE}, year = {2012}, author = {Ramesh Arti and Yoo Jaebong and Shen Shitian and Lise Getoor and Kim Jihie} } @article {sharara:snam10, title = {Understanding Actor Loyalty to Event-Based Groups in Affiliation Networks}, journal = {Journal of Advances in Social Networks Analysis and Mining}, volume = {1}, number = {2}, year = {2011}, month = {April}, pages = {115{\textendash}126}, author = {Sharara, Hossam and Singh, Lisa and Lise Getoor and Mann, Janet} } @conference {pujara:ceas11, title = {Using Classifier Cascades for Scalable E-Mail Classification}, booktitle = {Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference}, series = {ACM International Conference Proceedings Series}, year = {2011}, note = {Winner of a Best Paper award}, publisher = {ACM}, organization = {ACM}, author = {Pujara, Jay and Daume III, Hal and Lise Getoor} } @conference {zheleva:snakdd08, title = {Using Friendship Ties and Family Circles for Link Prediction}, booktitle = {2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD)}, year = {2008}, author = {Zheleva, Elena and Lise Getoor and Golbeck, Jennifer and Kuter, Ugur} } @article {getoor:aimj04, title = {Understanding Tuberculosis Epidemiology Using Probabilistic Relational Models}, journal = {AI in Medicine Journal}, volume = {30}, year = {2004}, pages = {233-256}, author = {Lise Getoor and Rhee, Jeanne and Koller, Daphne and Small, Peter} } @conference {bhattacharya:acl04, title = {Unsupervised Sense Disambiguation using Bilingual Probabilistic Models}, booktitle = {Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2004}, month = {July}, author = {Bhattacharya, Indrajit and Lise Getoor and Bengio, Yoshua} } @conference {lerman:sigmod04, title = {Using the Structure of Web Sites for Automatic Segmentation of Tables}, booktitle = {In Proceedings of ACM-SIGMOD 2004 International Conference on Management of Data}, year = {2004}, author = {Lerman, Kristina and Lise Getoor and Minton, Steve and Knoblock, Craig} } @conference {desjardins:sara00, title = {Using Feature Hierarchies in Bayesian Network Learning}, booktitle = {Symposium on Abstraction, Reformulation and Approximation}, year = {2000}, author = {desJardins, Marie and Lise Getoor and Koller, Daphne} } @conference {getoor:webkdd99, title = {Using Probabilistic Relational Models for Collaborative Filtering}, booktitle = {Working Notes of the KDD Workshop on Web Usage Analysis and User Profiling}, year = {1999}, author = {Lise Getoor and Mehran Sahami} } @conference {chajewska:mdm98, title = {Using Classi cation Techniques for Utility Elicitation: A Comparison between StandardGamble and Visual Analog Scale Methods}, booktitle = {Twentieth Anniversary Meeting of the Society for Medical Decision Making}, year = {1998}, author = {Chajewska, Ursulza and Norman, Joseph and Lise Getoor} } @conference {chajewska:uai98, title = {Utility Elicitation as a Classi cation Problem}, booktitle = {Uncertainty in Arti cial Intelligence}, year = {1998}, author = {Chajewska, Ursulza and Lise Getoor and Norman, Joseph and Shahar, Yuval} } @conference {chajewska:aaaiss98, title = {Utility Elicitation as a Classification Problem}, booktitle = {Proceedings of the AAAI Spring Symposium Series on Interactive and Mixed Initiative Decision-Theoretic Systems}, year = {1998}, author = {Chajewska, Ursulza and Lise Getoor and Norman, Joseph} }