Dan Schwartz, Stanford University, U.S.A.


Interactivity and Learning

-Brief biography -
Daniel Schwartz is an Associate Professor in the Learning Sciences, Technology and Design program at Stanford University, School of Education. Before completing his Ph.D. at Columbia University, Dr.  Schwartz worked eight years as a secondary school teacher in Kenya, the inner-city of Los Angeles, and a Native Alaskan village.  His research examines issues of transfer and how people move from untutored mental models to more formal and verbal understanding in the domains of mathematics, mechanics, and biology.  His work employs laboratory and computer-modeling methodologies, as well as classroom interventions that involve the use of instructional software programs that he has co-authored including STAR.Legacy and Teachable Agents.  More information about his research and publications may be found at the website.

- Abstract -
Two claims for artificial intelligence techniques in education are that they can increase positive interactive experiences for students, and they can enhance learning.  Depending on one’s preferences, the critical question might be “how do we configure interactive opportunities to optimize learning?”  Alternatively, the question might be, “how do we configure learning opportunities to optimize positive interactions?”  Ideally, the answers to these two questions are compatible so that desirable interactions and learning outcomes are positively correlated.  But, this does not have to be the case – interactions that people deem negative might lead to learning that people deem positive, or vice versa.  The question for this talk is whether there is a “sweet spot” where interactions and learning complement one another and the values we hold most important.  I will offer a pair of frameworks to address this question: one for characterizing learning by the dimensions of innovation and efficiency; and one for characterizing interactivity by the dimensions of initiative and idea incorporation. I will provide empirical examples of students working with intelligent computer technologies to show how desirable outcomes in both frameworks can be correlated.




Tanja Mitrovic, University of Christchurch, New Zealand


Title: Constraint-based tutors: a success story

- Brief biography -
Antonija Mitrovic is an Associate Professor at the Department of Computer Science and Software Engineering, the University of Canterbury, New Zealand. She received her BEng (1987), MSc (1991) and PhD (1994) degrees from the University of Nis, Yugoslavia. Her main research interest is the application of artificial intelligence techniques to education. She has authored and co-authored more than 90 research papers published in international and national journals and conferences. She is the leader of the Intelligent Computer Tutoring Group, where a new methodology for developing intelligent tutoring systems has been developed. Several ITSs based on this methodology have been evaluated in real classrooms since 1998, and the results show that these systems have a significant positive effect on students' learning.

- Abstract -
Constraint-based modelling (CBM) was proposed in 1992 as a way of overcoming the intractable nature of student modelling. Originally, Ohlsson viewed CBM as an approach to developing short-term student models. In this talk, I will illustrate how we have extended CBM to support both short- and long-term models, and developed methodology for using such models to make various pedagogical decisions. In particular, I will present several successful constraint-based tutors built for a various procedural and non-procedural domains. I will illustrate how constraint-based modelling supports learning and meta-cognitive skills, and present current project within the Intelligent Computer Tutoring Group.




Justine Cassell, Northwestern University, U.S.A.


Title: Learning with Virtual Peers

- Brief biography -
Justine Cassell is a full professor in the departments of Communication Studies and Computer Science at Northwestern University, the director of the ArticuLab research group, and the graduate director of the interdisciplinary Technology and Social Behavior Ph.D. program.  Before coming to Northwestern, Cassell was a tenured associate professor at the MIT Media Lab where she directed the Gesture and Narrative Language Research Group.  In 2001, Cassell was awarded the Edgerton Faculty Achievement Award at MIT.

Cassell holds undergraduate degrees in Comparative Literature from Dartmouth and in Lettres Modernes from the Universite de Besançon (France). She holds a M.Phil in Linguistics from the University of Edinburgh (Scotland) and a double Ph.D. from the University of Chicago in Linguistics and Psychology.

Cassell's research concentrates on better understanding everyday kinds of conversation and narrative as practiced by children and adults, and on building technologies that simulate, mediate, and facilitate those everyday kinds of talk.  These technologies, such as Embodied Conversational Agents, Story Listening Systems, and Online Communities, in turn allow her to study the nature of human communication with and through technology.

- Abstract -
Schools aren't the only places people learn, and in the field of educational technology, informal learning is receiving increasing attention.  In informal learning peers are of primary importance.  But, how do you discover what works in peer learning? If you want to discover what peers do for one other so that you can then set up situations and technologies that maximize peer learning, where do you get your data from?  You can study groups of children and hope that informal learning will happen and hope that you have a large enough sample to witness examples of each kind of peer teaching that you hope to study.

Or you can make a peer  Unfortunately, the biological approach takes years, care and feeding is expensive, diary studies are out of fashion, and in any case the human subjects review board frowns on the kind of mind control that would allow one to manipulate the peer so as to provoke different learning reactions.  And so, in my own research, I chose to make a bionic peer.

In this talk I describe the results from a series of studies where we manipulate a bionic peer to see the effects of various kinds of peer behavior on learning.  The peer is sometimes older and sometimes younger than the learners, sometimes the same race and sometimes a different race, sometimes speaking at the same developmental level -- and in the same dialect -- and the learners, and sometimes differently.  In each case we are struck by how much learning occurs when peers play, how learning appears to be potentiated by the rapport between the real and virtual child, and how many lessons we learn about the more general nature of informal learning mediated by technology.




Ton de Jong, University of Twente, Netherlands


Title: Scaffolding inquiry learning; How much intelligence is needed and by whom?

- Brief biography -
Ton de Jong studied cognitive psychology at the University of Amsterdam and received a PhD from the Eindhoven University of Technology on the topic ‘problem solving and knowledge representation in physics for novice students’. Currently he is full professor of Educational Psychology at the University of Twente, Faculty of Behavioral Sciences. In 2001/2002 he has also been (part-time) full professor at the Institute for Knowledge Media at the University of Tübingen (Germany). His main interests are in problem solving in science, inquiry (computer-simulation based) learning environments, learners’ cognitive processes, instructional design, and man-machine interface. He was project manager of the EC-telematics SERVIVE project in which SIMQUEST) an authoring tool for the creation of integrated simulation learning environments was developed. Ton de Jong was coordinator of a multi-university project sponsored by the Dutch NSF on modelling discovery behaviour. He was project manager of the EC (5th framework) KITS project (game-based learning of knowledge management) and was project manager of the EC (5th framework)  Co-Lab project (collaborative learning in virtual laboratories in science), and of the Dutch SURF ZAP project (interactive visuals for psychology). Currently he is on the executive committee of the EC (6th framework) Network of Excellence “Kaleidoscope”. He is associate editor of Instructional Science, and on the editorial boards of the Journal of Computer Assisted Learning, Contemporary Educational Psychology, and the Journal of Research in Science Teaching.

- Abstract -
Inquiry learning is way of learning in which learners act like scientists and discover a domain by employing processes such as hypothesis generation, experiment design, and data interpretation. The sequence of these learning processes and the choice for specific actions (e.g., what experiment to perform) are determined by the learners themselves. This student centeredness makes that inquiry learning heavily calls upon metacognitive processes such as planning and monitoring. These inquiry and metacognitive processes make inquiry learning a demanding task. When inquiry is combined with modelling and collaboration facilities the complexity of the learning process even increases. To make inquiry learning successful, the inquiry (and modelling and collaborative) activities need to scaffolded. Scaffolding can mean that the learning environment is structured or that  learners are provided with cognitive tools for specific activities. AI techniques can be used to make scaffolds more adaptive to the learner or to developments in the learning process. In this presentation an overview of (adaptive and non-adaptive) scaffolds for inquiry learning in simulation based learning environments will be discussed.details will follow.



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