ISLG

5th Workshop on Intelligent Support for Learning in Groups
Contact
Jennifer Olsen : jkolsen@cs.cmu.edu
Ilya Goldin : ilyagoldin@gmail.com
Roberto Martinez : roberto@it.usyd.edu.au
Erin Walker : erin.a.walker@asu.edu
Jihie Kim : jihie.kim@gmail.com
at the 13th International Conference on Intelligent Tutoring Systems (ITS 2016)
June 6-10, 2016
Zagreb, Croatia

Goals

Important Dates

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Proceedings (PDF)

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We are pleased to announce that the Fifth International Workshop on Intelligent Support for Learning in Groups (ISLG) will be held as a part of the 13th International Conference on Intelligent Tutoring Systems (ITS 2016) in Zagreb, Croatia. The goal of this series of workshops is to bring together Artificial Intelligence in Education (AIED) researchers with learning sciences (LS) and computer-supported collaborative learning (CSCL) researchers to share approaches and exchange information about adaptive intelligent collaborative learning support.

Technological advances in the use of educational and intelligent tutoring technologies over the past decades have enabled the development of highly effective, deployable learning environments that support learners in engaging in a variety of learning activities across a wide range of domains. Alongside, mass access to and adoption of modern communication technologies have made it possible to bridge learners and educators across spatiotemporal divides. As educational service providers, including schools and universities, deploy a variety of educational technologies in online and face-to-face settings, and large volumes of educational content including videos, presentations, books and games are accessible on mobile/tablet devices, students can now collaborate using educational technology in ways that were not previously possible. In addition, technology is often used in a collaborative way where students work together, even when the technology was not developed with that purpose in mind.

Intelligent tutoring systems (ITS) seek to individualize each student's learning experience, but this need not imply a solitary learner experience. Research in the learning sciences (LS) on computer-supported collaborative learning (CSCL) has revealed the pedagogical benefits of learning in groups, as well as how to structure the activity to lead to productive interactions. Learning in groups is not only beneficial for the learner, but it also encourages the student to externalise their internal thoughts and cognitive process, facilitating capture of evidence of the learning process. A variety of recent systems have demonstrated how an adaptive learning environment can incorporate and benefit from the presence of multiple learners. Similarly, students using CSCL systems have been shown to benefit from the introduction of adaptive support that targets and improves the collaboration.

It is of high relevance to the ITS community to explore how artificial intelligence, educational data mining and learning analytics can be brought to bear to support collaborative learning, and how theories of how students learn in groups can inform the design of adaptive educational technologies. The goal of this series of workshops is to gather the sub-community of ITS researchers interested in intelligent support for learning in groups with learning scientists and CSCL researchers to share approaches and exchange information about adaptive intelligent collaborative learning support.

In this workshop, we invite discussion and seek to explore ways in which the combination of collaborative and intelligent aspects of a system can benefit the learner by creating a more productive learning environment. Over the past few years, the ITS research community has started investigating extension of the fundamental techniques (student modeling, model-based tutors, integrated assessment, tutorial dialog, automated scaffolding, data mining, pedagogical agents, and so on) to support collaborative learning in groups. We aim to explore ways that the current state of the art in intelligent support for learning in groups can be informed by learning sciences research on collaborative learning principles as well as how to expand these principles to technology and data collection with technology where collaboration may not have been originally envisioned.

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