Follow the talks of our Keynote speakers in these recordings :

Keynote #1

Academic Analytics – Analysis and Mining of Educational Data to Support

Sabine Graf, Athabasca University, Canada

Offerings of online courses and even entire online programs are continually increasing in numbers and popularity. To deliver online courses to students, learning systems are used which collect and store huge amounts of data about how students behave, learn and perform in such courses. However, although such data can provide a wealth of relevant and actionable information for people involved in the learning and teaching process (e.g., educators, learning designers, students, etc.), such data are very rarely used. In this talk, I will focus on how such data can be utilized to support teaching. I will discuss the kind of information that can be extracted from such data and the potential of this data to support the teaching process. I will also introduce some of the systems, tools and plugins that have been designed, developed and evaluated within my research group. Those systems, tools and plugins use different approaches to support teaching. One of those approaches is to use artificial intelligence and data mining techniques to mine data about students’ behavior and actions in a course and provide specific, relevant and timely information to educators. Another approach is to use academic and learning analytics techniques to guide educators through extracting and analyzing relevant information from the huge amounts of data stored in learning systems and letting them conduct their own investigations into any questions they are interested in getting more information about. For both approaches, examples of systems, tools and plugins will be shown during the talk.

About Dr Sabine Graf is a Full Professor at Athabasca University, School of Computing and Information Systems, in Canada. Her research aims at making information systems, especially learning systems, more personalized, intelligent and adaptive. In addition, her research focuses on enabling such systems to make use of the huge amounts of data the systems collect to provide decision makers (e.g., students, teachers, etc.) with actionable information. Her research expertise and interests include learning analytics, academic analytics, adaptivity and personalization, student modeling, artificial intelligence, and game-based learning. She has published more than 130 peer-reviewed journal papers, book chapters, and conference papers in these areas which have been cited over 5,000 times and four conference papers were awarded with a best paper award. Dr Graf has been invited to present her research findings in keynote/invited talks at universities, companies and conferences around the world. Furthermore, Dr Graf is steering committee member of IEEE Transactions on Learning Technologies, associate editor of two international journals, editorial board member of six international journals and guest editor of five special issues. She has also been chair and organizer of numerous conference tracks, international workshops and doctoral consortiums.

Keynote #2

Machine learning for online education
Irwin King, The Chinese University of Hong Kong

With the advent of online education, students from all over the world can access quality education contents from anywhere and anytime. Due to this great convenience, we are able to collect a massive volume of learning activity data. In this talk, we introduce how machine learning techniques can improve teaching and learning for educators and students using the collected learning activity data. First, I will present a few machine learning models and algorithms for peer assessment. Peer assessment offers a scalable way to grade tens of thousands of assignments in Massive Open Online Courses (MOOCs) by asking each student to grade a subset of his/her peer’s assignment. Since these peer grades are very noisy, we need robust machine learning techniques to recover the ground truth scores or an overall ranking of all the assignments. Next, I will present machine learning models for knowledge tracing. Knowledge tracing is a task of tracing evolving knowledge states of students with respect to different concepts as they engage in a sequence of learning activities. The goal is to personalize the practice sequence to help students learn knowledge concepts more efficiently. Lastly, I will share our KEEP (Knowledge and Education Exchange Platform) education project for teaching and learning in Hong Kong and beyond.

About Prof. King’s research interests include machine learning, social computing, AI, web intelligence, data mining, and multimedia information processing. In these research areas, he has over 300 technical publications in various top journals and conferences. He is an Associate Editor of the ACM Transactions on Knowledge Discovery from Data (ACM TKDD) and Journal of Neural Networks. He is President of the International Neural Network Society (INNS), IEEE Fellow, and HKIE Fellow. Moreover, he is the General Co-chair of The WebConf 2020 in Taipei, ICONIP2020 in Hong Kong, WSDM2011, RecSys2013, ACML2015, and in various capacities in a number of top conferences such as WWW, NIPS, ICML, IJCAI, AAAI, etc. Prof. King is the former Associate Dean (Education), Faculty of Engineering and Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He was on leave with AT&T Labs Research, San Francisco and taught Social Computing and Data Mining as a Visiting Professor at UC Berkeley.

Recently, Prof. King has been an evangelist in the use of education technologies in eLearning for the betterment of teaching and learning through the creation of the Knowledge and Education Exchange Platform (KEEP). He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology (Caltech), Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California (USC), Los Angeles.

Keynote #3

The usefulness of search results and task outcome
Prof. Dr. Pertti Vakkari, University of Tampere, Finland

In evaluating search systems there is a growing trend to complement the established effectiveness indicator topical relevance by the usefulness of search results. Usefulness refers to the contribution of search results to a larger task generating information search. Task outcome signifies the end-product of a task performance process. It is evident that the whole search process contributes to task outcome. The aim of my presentation is to give a systematic account of the characteristics and results of studies focusing on the usefulness of search results or task outcomes in larger tasks containing searching. I summarize how the usefulness of search results, and search outcomes are defined and operationalized in empirical studies. I categorize various types of usefulness and outcomes and various predictor types used in studies. Finally, I summarize which factors significantly predict the usefulness of search results as well as search outcomes.