Dr. Art Graesser: “Scaling Up Learning by Communicating with AutoTutor, Trialogs, and Pedagogical Agents”

University of Memphis


graesserLearning has occurred over a few millennia by the learner communicating with the teacher, tutor, master, or mentor in natural language. Apprenticeship learning has always occurred one-on-one or in small groups with an expert. Researchers in the discourse and learning sciences have documented the conversation patterns that occur in these interactions. Researchers in computational linguistics, artificial intelligence, and intelligent tutoring systems have developed computer agents that simulate many of these conversation patterns and help people learn. This is the moment in history when these systems are being launched on the internet.

This presentation will present recent systems on the internet that help students learn by holding a conversation in natural language. AutoTutor engages in dialogue with the student on a variety of subject matters in Science, Technology, Engineering, and Mathematics. Trialogs are conversations between the human students and two computer agents, typically a student agent and a tutor agent. Students can either observe two agents interact vicariously, interact with a tutor agent as a student agent periodically chimes in, or teach a student agent while a tutor rescues a problematic interaction. Agents can argue with each other over issues and ask what the human students think about the argument. Trialogs are being developed for the Internet in serious games with Pearson Education (Operation ARA), in assessments with Educational Testing Service, and in a new Center for the Study of Adult Literacy for struggling adult readers. AutoMentor is being developed for computer mediated communication between a mentor agent and small groups of students in a simulation game on urban planning. Tests of these systems have shown very encouraging learning gains. We are currently conducting data mining analyses to explore the features of conversation and learning experiences that predict learning, motivation, and student emotions.


Dr. Art Graesser is a professor in the Department of Psychology and the Institute for Intelligent Systems at the University of Memphis, as well as an Honorary Research Fellow at University of Oxford. He received his Ph.D. in psychology from the University of California at San Diego. His primary research interests are in cognitive science, discourse processing, computational linguistics, and the learning sciences. More specific interests include knowledge representation, question asking and answering, tutoring, text comprehension, inference generation, conversation, reading, education, memory, artificial intelligence, and human-computer interaction. He served as editor of the journal Discourse Processes (1996–2005) and is the current editor of Journal of Educational Psychology. His service in professional societies includes president of the Empirical Studies of Literature, Art, and Media (1989-1992), the Society for Text and Discourse (2007-2010), the International Society for Artificial Intelligence in Education (2007-2009), and the Federation of Associations in the Behavioral and Brain Sciences Foundation (2012-13). In addition to publishing over 500 articles in journals, books, and conference proceedings, he has written two books and edited 12 books. He and his colleagues have designed, developed, and tested software in learning, language, and discourse technologies, including AutoTutor, AutoTutor-lite, MetaTutor, GuruTutor, DeepTutor, HURA Advisor, SEEK Web Tutor, Operation ARIES!, iSTART, Writing-Pal, Point & Query, Question Understanding Aid (QUAID), QUEST, & Coh-Metrix.

Dr. Nancy Law: “Is Learning Analytics a Disruptive Innovation?”

University of Hong Kong


nancy law
Description: Disruptive technologies have the potential to fundamentally change the ecological terrain of a market, not because they have outstanding performance or impact at its debut, but because they have features that enable them to address new needs and bring about totally different ways of (imagining) operations, new relationships and new services (Christensen, 1997). Learning analytics (LA) has all the features of a disruptive innovation. It is potentially empowering learners to have a much better understanding of their own progress and characteristics as a learner in relation to their own aspirations and their peers (in many different senses of the word), and on that basis to receive recommendations on further steps to take on their learning pathways. LA systems may provide for teachers real-time information about learner progress as well as feedback on effectiveness of different pedagogical approaches and learning designs. It may serve as an alternative methodology for developing theories about human learning. A lot has also been written about how institutions and whole school districts may make use of LA to track progress and make use of such information in multiple ways that could have significant implications at many levels. The LA research community is well aware of the many technological challenges yet to be addressed before these possibilities can be realized. However, given the very impressive developments in the short history of LA as a research field, and in particular the intentional efforts of those in the related disciplines to forge interdisciplinary partnership and to engage in multivocal dialogues, the technological futures for LA is a rosy one. On the other hand, if we review the last three decades of development in technology enhanced learning (TEL), a closely related field of research and innovation, it is reasonable to conclude that the promises of currently available TEL solutions are far from having gained a significant foothold in mainstream educational settings, despite the wide acceptance and support at the policy level. It is only in recent years that the disruptive nature of TEL as a socio-technical innovation and its implications for TEL implementation strategies is recognized. This paper discusses the challenges to and possible strategies for scalable development and adoption of LA, drawing on the literature as well as the author’s own research in international comparative studies on sustaining ICT enabled pedagogical innovations and design-based research with K-12 teachers on integrating LA with e-learning design.


Professor Nancy Law is currently Deputy Director of the Centre for Information Technology in Education (CITE) in the Faculty of Education of the University of Hong Kong, after serving as its Founding Director for 15 years from 1998. She serves/has served on a number of policy advisory boards/working groups related to ICT in education for the University of Hong Kong, the Hong Kong government and other community groups. She was a core member of the International Study Centre for the SITES 2006 (Second Information Technology in Education Study 2006), served on the Editorial and Publication Committee of the IEA and the Technology Working Group of the Cisco-Intel-Microsoft Project on Assessment and Teaching of 21st Century Skills, the Board of Directors of the International Society of the Learning Sciences, the Editorial Boards of the International Journal of Computer-Supported Collaborative Learning and the International Journal of Web Based Communities. She has also been invited to provide expert input/consultancy to the European Commission, UNESCO and OECD on various aspects of technology-enhanced learning. Her research interests include international comparative studies of pedagogical innovations and information technology, models of ICT integration in schools and change leadership, computer supported collaborative learning and the use of expressive and exploratory computer-based learning environments.

Dr. Scott Klemmer: “Design at Large”

University of California San Diego


This is the most exciting time of my career so far. Learning, design, and technology are on the front page of the news and the topic of Hollywood films. Enrollments in our classes have skyrocketed, both in person and online. For me, the most powerful dynamic of the current moment is the large number of people who are excited about making stuff.

This current wave of enthusiasm offers a tremendous opportunity for durable impact, but also a challenge. I’ve spent a lot of time with design teachers over the past two decades. While the design fields have had enormous impact, I think nearly everyone who has taught design has wished for more and better theory (vocabulary, principles, predictive power) to help coach students towards expertise. Why the gap? First, many see design as a mystical endeavor because creative work is clearly complex and multifarious. Consequently, many people give up on creating theory — or seeking to apply/adapt existing theory — without trying on the belief that it’s prima facie impossible. Second, much of the theory I’m talking about here is really social science. At many universities, the social sciences are across campus from the arts and engineering programs that house design, and even further away intellectually.

In this talk, I’ll share our adventures in facilitating peer interactions in online education. In person, communities of peers, shoulder-to-shoulder, can create powerful learning experiences. For example, hallmark traits of design education are studio critique and working in a collocated physical space. With online learning, are students “alone together” — viewing the same materials with no peer contact?

In 2012, my research group collaborated with Coursera to launch the first massive-scale class with self and peer assessment. Since then, more than 80 other massive online classes have featured peer assessment — from world music to nutrition to mathematical thinking. On the whole, it has worked surprisingly well. I’ll also share challenges and failures, and talk about integrating peer and automated assessment.

This led us to experiments with small group video discussion, social networks, and global meet-ups — also deploying our systems in collaboration with teachers from diverse fields. My goal is to offer techniques that attendees can adapt for their own teaching and avoid our missteps. And I look forward to hearing more about the adventures of others.

More broadly, because online learning platforms embed pedagogy into software, they provide a powerful setting for using and building theory through experimentation. These online learning interventions illustrate how design at large — at scale, embedded in real-world activities, and occasionally subversive — can be a potent and relevant research strategy.


Scott is an Associate Professor of Cognitive Science and Computer Science & Engineering at UC San Diego, and a Visiting Associate Professor of Computer Science at Stanford. He previously served as an Associate Professor of Computer Science at Stanford, where he co-directed the Human-Computer Interaction Group and held the Bredt Faculty Scholar chair. He has a dual BA in Art-Semiotics and Computer Science from Brown (with studio work at RISD), and a PhD in Computer Science from Berkeley. The open-source design tools and curricula created in his lab have been adopted by organizations around the world. He has served on advisory boards for design programs (like CCA), research labs (like DoCoMo), and startups. His former graduate students are leading professors (including Berkeley, CMU, and UIUC), researchers (at Adobe), founders (including Instagram and Pulse, …), social entrepreneurs, and engineers. He helped introduce peer assessment to open online education, and taught the first peer-assessed online course. He has been awarded the Katayanagi Emerging Leadership Prize, Sloan Fellowship, NSF CAREER award, and Microsoft Research New Faculty Fellowship. He has authored and co-authored more than 40 peer-reviewed articles; eight were awarded best paper or honorable mention at the premier HCI conferences (CHI/UIST/CSCW).