Sankaranarayanan, Sreecharan (Carnegie Mellon University) | Tomar, Gaurav Singh (Carnegie Mellon University) | Wen, Miaomiao (Carnegie Mellon University) | Bharadwaj, Akash (Carnegie Mellon University) | Rosé, Carolyn Penstein (Carnegie Mellon University)
Despite studies showing collaboration to be beneficial both in terms of student satisfaction and learning, isolation is the norm in MOOCs. Two problems limiting the success of collaboration in MOOCs are the lack of support for team formation and structured collaboration support. Lack of support and strategies for team formation prevents teams from being set up for success from the beginning. Lack of structured support during synchronous collaboration has been demonstrated to produce significantly less learning than supported collaboration. This paper describes a deliberation based team formation approach and a scripted collaboration framework for MOOCs aimed at addressing these problems under the umbrella of Discussion Affordances for Natural Collaborative Exchange (DANCE) whose overarching focus is the enhancement of team-based MOOCs. These two examples of current work have been used as illustrations of insights informing interventions in MOOCs.
Andrew Yan-Tak Ng (Chinese: 吳恩達; born 1976) is a Chinese-American computer scientist and statistician, focusing on machine learning and AI. Also a business executive and investor in the Silicon Valley, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people. Ng is an adjunct professor at Stanford University (formerly associate professor and Director of its AI Lab). Also a pioneer in online education, Ng co-founded Coursera and deeplearning.ai. With his online courses, he has successfully spearheaded many efforts to "democratize deep learning."
A voting center is in charge of collecting and aggregating voter preferences. In an iterative process, the center sends comparison queries to voters, requesting them to submit their preference between two items. Voters might discuss the candidates among themselves, figuring out during the elicitation process which candidates stand a chance of winning and which do not. Consequently, strategic voters might attempt to manipulate by deviating from their true preferences and instead submit a different response in order to attempt to maximize their profit. We provide a practical algorithm for strategic voters which computes the best manipulative vote and maximizes the voter's selfish outcome when such a vote exists. We also provide a careful voting center which is aware of the possible manipulations and avoids manipulative queries when possible. In an empirical study on four real-world domains, we show that in practice manipulation occurs in a low percentage of settings and has a low impact on the final outcome. The careful voting center reduces manipulation even further, thus allowing for a non-distorted group decision process to take place. We thus provide a core technology study of a voting process that can be adopted in opinion or information aggregation systems and in crowdsourcing applications, e.g., peer grading in Massive Open Online Courses (MOOCs).
LinkedIn wants to become more useful to workers by adding personalized news feeds, helpful messaging "bots" and recommendations for online training courses, as the professional networking service strives to be more than just a tool for job-hunting. The new services will arrive just as LinkedIn itself gains a new boss -- Microsoft -- which is paying 26 billion to acquire the Silicon Valley company later this year. LinkedIn said the new features, which it showed off to reporters Thursday, were in the works before the Microsoft takeover was announced in June. But LinkedIn CEO Jeff Weiner said his company hopes to incorporate some of Microsoft's technology as it builds more things like conversational "chat bots," or software that can carry on limited conversations, answer questions and perform tasks like making reservations. Chat bots are a hot new feature in the consumer tech world, where companies like Facebook, Apple and Google are already racing to offer useful services based on artificial intelligence.
About this course: This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our "Bayesian toolbox" with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution.