- 09:00 – 09:10 Welcome and introduction
- 09:10 – 10:00 Selected paper presentations
- 09:10 – 09:35 Georg Pardi, Yvonne Kammerer and Peter Gerjets. Search and Justification Behavior During Multimedia Web Search for Procedural Knowledge.
- 09:35 – 10:00 Thi-Huyen Nguyen, Tuan-Anh Hoang, Wolfgang Nejdl. Efficient Summarizing of Evolving Events from Twitter Streams. (invited talk)
- 10:00 – 10:30 Break
- 10:30 – 12:00 Keynote 1 by Prof. Dr. Carolyn Penstein Rose
- 12:00 – 13:00 Lunch
- 13:00 – 14:30 Keynote 2 by Prof. Dr. Kevyn Collins-Thompson
- 14:30 Closing of the workshop, social events
Keynote 1: Mining Web Scale Interaction Data in Support of Collaborative Learning
Dr. Carolyn Rosé is a Professor of Language Technologies and Human-Computer Interaction in the School of Computer Science at Carnegie Mellon University. Her research program is focused on better understanding the social and pragmatic nature of conversation, and using this understanding to build computational systems that can improve the efficacy of conversation between people, and between people and computers. In order to pursue these goals, she invokes approaches from computational discourse analysis and text mining, conversational agents, and computer supported collaborative learning. Her research group’s highly interdisciplinary work, published in over 220 peer reviewed publications, is represented in the top venues in 5 fields: namely, Language Technologies, Learning Sciences, Cognitive Science, Educational Technology, and Human-Computer Interaction, with awards in 3 of these fields. She serves as Past President and Inaugural Fellow of the International Society of the Learning Sciences, Senior member of IEEE, Founding Chair of the International Alliance to Advance Learning in the Digital Era, Executive Editor of the International Journal of Computer-Supported Collaborative Learning, and Associate Editor of the IEEE Transactions on Learning Technologies.
Abstract: This talk reports on over a decade of research where theoretical foundations motivate computational models that produce real world impact in online spaces. Both the earliest philosophers of language and the most recent researchers in computational approaches to social media analysis have acknowledged the distinction between the what of language, namely its propositional content, and the how of language, or its form, style, or framing. What bridges between these realms are social processes that motivate the linguistic choices that result in specific realizations of propositional content situated within social interactions, designed to achieve social goals. These insights allow researchers to make sense of the connection between discussion processes and outcomes from those discussions. These findings motivate on the one hand design of computational approaches to real time monitoring of discussion processes and on the other hand the design of interventions that support interactions in online spaces with the goal of increasing desired outcomes, including learning, health, and wellbeing. As an example, in this talk we probe into a specific quality of discussion referred to as Transactivity. Transactivity is the extent to which a contribution articulates the reasoning of the speaker, that of an interlocutor, and the relation between them. In different contexts, and within very distinct theoretical frameworks, this construct has been associated with solidarity, influence, expertise transfer, and learning. Within the construct of Transactivity, the cognitive and social underpinnings are inextricably linked such that modeling the who enables prediction of the connection between the what and the how.
Keynote 2: Connecting Searching with Learning
Prof. Dr. Kevyn Collins-Thompson is an Associate Professor of Information and Computer Science at the University of Michigan. His research explores models, algorithms, and software systems for optimally connecting people with information, especially toward educational goals. His research has been applied to real-world systems ranging from intelligent tutoring systems to commercial Web search engines. Kevyn has also pioneered techniques for using machine learning to model the reading difficulty of text, risk-averse search ranking algorithms that maximize effectiveness while minimizing worst-case errors, and understanding and supporting how people learn language. He received his Ph.D. from the Language Technologies Institute at Carnegie Mellon University, where his advisor was Jamie Callan. Before joining the University of Michigan in 2013, he was a researcher in the Context, Learning, and User Experience for Search (CLUES) group at Microsoft Research. Highlights from the past year include serving as ACM SIGIR 2018 General Co-Chair, and being named co-recipient of Coursera’s Outstanding Educator Award (for Innovation).
Abstract: Although search engines are one of the most widely-used methods that people use to learn and explore, current search technology has been optimized (mostly) for generic relevance, not the background and learning needs of specific users. As a result, users often do not get effective access to the materials best suited for their learning needs. Moreover, little is known about the relationship between online search and interaction over time and actual learning outcomes. With collaborators, I have been exploring ways that search engines and rich online content and interaction signals can help us understand and support human learning goals, broadly defined. This talk will summarize progress in that direction from a range of research projects over the past decade, including exploration of richer learning-oriented representations of users and Web content (e.g. what happens if you label billions of Web pages with reading difficulty metadata?), new types of search ranking algorithms that try to directly optimize for learning goals, evaluating implicit online measures of learning, and user studies exploring the relationship between search quality, interaction patterns, and learning outcomes.