Time：19 May, 2017 (Friday) 9:00-18:00

Location: 清华主楼接待厅（Located at the second floor of Main Building of Tsinghua University）

9:00-9:15              Opening

9:15-10:00     Keynote1：Zvi Galil (GIT)

            Georgia Tech's Online MOOC-based Master Program

10:00-10:30  Coffee Break

10:30-11:00    Keynote2：Jian Guan (CTO, XuetangX)

            Big Data at XuetangX: From Statistics to Analytics

11:00-11:30    Keynote3：Tracy Liu (THU)

Incentive Design on MOOC: a Field Experiment on XuetangX

11:30-14:00  Lunch   

14:00-14:40  Keynote4：Juanzi Li (THU)

            Course Concept Discovery and Prerequisite Relation Learning in MOOCs

14:40-15:20  Keynote5：Michalis Vazirgiannis (PolyTech, France)           

            Graph degeneracy for measuring influence in academic data

15:20-15:40  Coffee Break

15:40-16:30  Keynote6：Maosong Sun (THU)

            TBD

16:30-17:10  Keynote7: Jimeng Sun (GIT)

Social Engagement Modeling for Massive Open Online Courses

17:10-17:50  Keynote8: Wei Xu/Jie Tang (THU)

            LittleMU: Enhancing Learning Engagement Using Artificial Intelligence

 

 

 

Bio: Dr. Zvi Galil, Dean of the College of Computing, Georgia Institute of Technology, was born in Tel-Aviv, Israel. He earned BS and MS degrees in Applied Mathematics from Tel Aviv University, both summa cum laude. He then obtained a PhD in Computer Science from Cornell University. After a post-doctorate in IBM's Thomas J. Watson research center, he returned to Israel and joined the faculty of Tel-Aviv University. He served as the chair of the Computer Science department in 1979-1982.  

In 1982 he joined the faculty of Columbia University. He served as the chair of the Computer Science Department in 1989-1994 and as dean of The Fu Foundation School of Engineering & Applied Science in 1995-2007. Galil was appointed Julian Clarence Levi Professor of Mathematical Methods and Computer Science in 1987, and Morris and Alma A. Schapiro Dean of Engineering in 1995. In 2007 Galil returned to Tel Aviv University and served as president. In 2009 he resigned as president and returned to the faculty as a professor of Computer Science. In July 2010 he became The John P. Imlay, Jr. Dean of Computing at Georgia Tech.   

Dr. Galil's research areas have been the design and analysis of algorithms, complexity, cryptography and experimental design. In 1983-1987 he served as chairman of ACM SIGACT, the Special Interest Group of Algorithms and Computation Theory. He has written over 200 scientific papers, edited 5 books, and has given more than 200 lectures in over two dozen countries. Galil has served as editor in chief of two journals and as the chief computer science adviser in the United States to the Oxford University Press. He is a fellow of the ACM and the American Academy of Arts and Sciences and a member of the National Academy of Engineering. In 2008 Columbia University established the Zvi Galil Award for Improvement in Engineering Student Life. In 2009 the Columbia Society of Graduates awarded him the Great Teacher Award.  In 2012 the University of Waterloo awarded him an honorary doctorate in mathematics. Zvi Galil is married to Dr. Bella S. Galil, a marine biologist. They have one son, Yair, a corporate lawyer in New York.

Title： Georgia Tech's Online MOOC-based Master Program

Abstract：In May 2013, Georgia Tech together with its partners, Udacity and AT&T, announced a new online master’s degree in computer science delivered through the platform popularized by massively open online courses (MOOCs). This new online MS CS— or OMSCS for short — costs less than $7,000 total, compared to a price tag of $40,000 for an MS CS at comparable public universities and upwards of $70,000 at private universities.

The first-of-its-kind program was launched in January 2014 and has sparked a worldwide conversation about higher education in the 21st century. President Barack Obama has praised OMS CS by name twice, and over 1,000 news stories mentioned the programs. It’s been described as a potential "game changer" and "the first real step in the transformation of higher education in the US.” Harvard University researchers concluded that OMSCS is “the first rigorous evidence … showing an online degree program can increase educational attainment” and predicted that OMSCS will singlehandedly raise the number of annual MS CS graduates in the United States by at least 7 percent.

To ensure program quality and rigor, Georgia Tech started in 2014 with small enrollment of 380; in January 2017, enrollment exceeded 4,500. So far 277 students have graduated from OMSCS, and another 300+ have registered to graduate in Spring 2017. The program has also paved the way for a number of similar, MOOC-based MS programs.

The talk will describe the OMSCS program, how it came about, its first three years, and what Georgia Tech has learned from the OMSCS experience. We will also discuss its potential effect on higher education.

 

 

 

Bio: Dr. Jian Guan is the CTO of xuetangx.com, the largest Chinese MOOC platform in the world. Previously, he has worked for IRTouch Systems Inc. (300282.SZ) and Microsoft. His research interests include machine learning, graphical model and image processing. Dr. Guan received his Ph.D in Computer Science from The University of Nottingham in 2008, and a degree of BEng from Tsinghua University in 1997. He is an Expert of IEC TC 110.

Title： Big Data at XuetangX: From Statistics to Analytics

Abstract：After 3 years of rapid growth, xuetangx.com has become the world's largest Chinese MOOC platform, which hosts more than 1,000 courses, serving 221 institutions and 7 million users. In this talk, we will briefly review the history of the website, and then discuss some of our practices, finally we will explore some of the directions for future efforts.

 

 

 

 

Bio: Tracy Liu is an associate Professor at School of Economics and Management, Tsinghua University. She received her Ph.D at University of Michigan-Ann Arbor. Her research interests are experimental and behavioral economics, as well as game theory. She has published in Management Science, Games and Economic Behavior and other top journals in economics and management.

Title：Incentive Design on MOOC: a Field Experiment on XuetangX

Abstract：Massive Open Online Courses (MOOCs) have become an inevitable trend for equalizing education resources and innovating education by providing people around the world the access to high-quality education resources in a highly interactive environment. However, how to maintain students’ learning enthusiasm and keeping them finishing courses are the central challenges for MOOC platforms. By conducting a field experiment on XuetangX, one of the largest MOOC platforms in China, we investigate the effect of reward size and the framing effect of delivering monetary reward on students’ learning behavior. We find that monetary incentive, especially large amount of reward and punishment, has positive and significant impact on student performance. Compared to control, the effects for large amount of reward, for example, amount for 173% increase in assignment grades for a low knowledge-based course and 76% for a high knowledgebased one. The treatment also leads to more effort exerted on learning activities, e.g., longer time spent watching lecture videos. Additionally, it could help them develop learning habit in the longrun.

 

 

 

Bio：Prof. Dr. Juanzi Li is a full professor at Tsinghua University. She obtained her PhD degree from Tsinghua University in 2000. Her main research interest is to study the semantic technologies by combining the key technologies of Natural Language Processing, Semantic Web and Data Mining. She is the vice director of Chinese Information Processing Society of Chinese Computer Federation in China. She is principal investigators of many key projects supported by Natural Science Foundation of China(NSFC), national basic science research program and international cooperation projects. She has published over 90 papers in many international journals and conferences such as TKDE, SIGIR, SIGMOD, SIGKDD, IJCAI, et al.。

Title：Course Concept Discovery and Prerequisite Relation Learning in MOOCs

Abstract：Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education. How to design a global, systematic and structured concept structure for online courses such that students with different backgrounds can easily explore the knowledge space and better design their personalized learning schedule is a challenging problem in MOOCs platform. In this talk, I will introduce our work in course concept extraction and prerequisite relationship detection. We first systematically investigate how to discover fine-grained course concepts from course videos in MOOC by incorporating external online encyclopedia corpus to learn the semantic representations of candidate course concepts, and proposing a graph-based confidence propagation algorithm to extract course concepts. We further propose several useful features from the  to help infer prerequisite relations between concepts in MOOCs and use Random Forest to effectively detect concept prerequisite relations in MOOCs.

 

 

 

Bio：He has established and leads the Data Science and Mining (DaSciM) Team at the Laboratory of Informatics at Ecole Polytechnique in France. The team current research interests are on Machine Learning and Combinatorial Methods for Graph Analysis (including Community Detection, Graph Clustering and Embeddings, Influence Maximization), Text Mining (including Graph of Words), Word Embeddings with applications to Web Advertising and Marketing, Event Detection, and Summarization from text streams.  He has active cooperation with industrial partners in the area of data analytics and machine learning for large scale data repositories in different application domains such as Web advertising and recommendations, social networks, medical data, aircraft logs, insurance data. Representative project partners include AIRBUS, Google, Paris School of Economics, AXA, BNP and others.  The highlights of his recent research results include  extensions of graph degeneracy for community detection and influence maximization, the novel Graph of Words approach for text mining tasks with excellent performance on retrieval, keyword extraction, summarization and event detection in streams. He has been invited to offer tutorials and keynote speeches in different international events on the above topics.  He has supervised 15 PhD theses, has contributed chapters in books and encyclopedias, published three books and more than a hundred fifty papers in international refereed journals and conferences.  His work on the D-core metric of collaboration and authority was adopted by the scientific portal aminer.org. His work on automated text generation was used to found a start up (nelper.io). Since 2015 M. Vazirgiannis leads the AXA Data Science chair. In the context of this chair Prof. Vazirgiannis organizes training events and data challenges in the context of data science for insurance problems.

Title：Graph degeneracy for measuring influence in academic data

Abstract：Community subgraphs are characterized by dense connections or interactions among its nodes. Community detection and evaluation is an important task in graph mining. A variety of measures have been proposed to evaluate the quality of such communities. In our research, we evaluate communities based on the k-core concept, as means of evaluating their collaborative nature - a property not captured by the single node metrics or by the established community evaluation metrics. Based on the k-core, which essentially measures the robustness of a community under degeneracy, we extend it to weighted graphs. We applied the k-core approach on large real world graphs - such as DBLP and report interesting results. We further extend introduce novel metrics for evaluating the collaborative nature of directed graphs and define a novel D-core framework, extending the classic graph-theoretic notion of k-cores for undirected graphs to directed ones. Based on the D-core, which essentially can be seen as a measure of the robustness of a community under degeneracy, we devise a wealth of novel metrics used to evaluate graph collaboration features of directed graphs. We applied the D-core approach on large real-world graphs such as Wikipedia and Aminer.org citation data and report interesting results. The D-core metric has been adopted by Aminer as part of its reported metrics - see an example here. As for influence maximization we investigate the potential of k-truss as a method to select the best single spreaders. The results are promising and show that starting an epidemic from the densest k-truss is beneficial. Finally we revisit the notion of influence in citation graphs – aiming at rethinking the hop-1 based impact that is dominant so far but apparently problematic. We report initial results showing that the influence of a paper (based on its citation tree) is well correlated to its core number.

 

 

 

Bio:清华大学计算机科学与技术系教授，博士生导师，党委书记。2007-2010年任该系系主任。现任清华大学大规模在线教育研究中心主任，教育部在线教育研究中心副主任，清华大学-新加坡国立大学下一代搜索技术联合研究中心共同主任。研究方向为自然语言理解、中文信息处理、Web智能、社会计算和计算教育学等。国家973计划项目首席科学家，国家社会科学基金重大项目首席专家。主持完成信息处理用分词国际标准2项。在国际刊物、国际会议、国内核心刊物上共发表论文130余篇，其中在Computational Linguistics、IEEE Intelligent Systems、ACM TALIP、IJCAI、AAAI、ACL、EMNLP、COLING、VLDB等国内外一流学术期刊和会议上发表论文数十篇。

 

 

 

Bio: Jimeng Sun is an Associate Professor of College of Computing at Georgia Tech. Prior to Georgia Tech, he was a researcher at IBM TJ Watson Research Center. His research focuses on health analytics and data mining, especially in designing tensor factorizations, deep learning methods, and large-scale predictive modeling systems. He published over 120 papers and filed over 20 patents (5 granted). He has received  SDM/IBM Early Career Data Mining Research Award 2017,  ICDM best research paper award in 2008,  SDM best research paper award in 2007, and KDD Dissertation runner-up award in 2008. Dr. Sun received B.S. and M.Phil. in Computer Science from Hong Kong University of Science and Technology in 2002 and 2003, M.Sc and PhD in Computer Science from Carnegie Mellon University in 2006 and 2007.

Title：Social Engagement Modeling for Massive Open Online Courses

Abstract: MOOCs boomed swiftly in recent years and have attracted millions of users worldwide. This is not only transforming higher education, but also provides fodder for scientific research. Despite the vast number of students being reached by MOOC, the overall success of MOOC as the delivery vehicle for future education is still in debate. The main challenges of MOOCs include: 1) low course completion rate with an average of 5% or lower2; 2) scalable and reliable assessments such as homework and exams are still lacking; and 3) deeper interactions between students and teachers are difficult to provide.

Thanks to MOOC’s online platforms, we are able to observe students behaviors and outcomes in such a fine-granularity that were never available before.  Our central research goal of this project is to design and validate analytic algorithms and prototype a software platform for enhancing student engagement from taking massive open online courses (MOOCs). The key of this project is on analyzing large amounts of historical student activity data from MOOCs and introducing effective social interactions and incentives to engage students. We will use the complete data from XuetangX as the input in our research and focus on proposing methodologies for modeling and predicting user behaviors, on designing new mechanisms to improve student engagement in MOOC, and on developing new ways to help user better understand course content.

 

 

Bio: Jie Tang is a Tenured associate professor with the Department of Computer Science and Technology at Tsinghua University, and was also visiting scholar at Cornell University, Hong Kong University of Science and Technology, and Southampton University. His interests include social network analysis, data mining and machine learning. He has published more than 200 journal/conference papers and holds 20 patents. His paper have been cited by more than 8,500 times. He served as PC Co-Chair of CIKM'16, WSDM'15, ASONAM'15, SocInfo'12, KDD-CUP/Poster/Workshop/Local/Publication Co-Chair of KDD'11-15, and Editor-in-Chief of ACM TKDD, Editors of IEEE TKDE/TBD and ACM TIST. He leads the project AMiner.org for academic social network analysis and mining, which has attracted more than 8 million independent IP accesses from 220 countries/regions in the world. He was honored with the UK Royal Society-Newton Advanced Fellowship Award, CCF Young Scientist Award, and NSFC Excellent Young Scholar.