Rethinking Intelligent Support for Learning in Groups
June 23rd, 2018
Workshop to be held at the International Conference of the Learning Sciences, ICLS'18
as part of the The Festival of Learning, London, UK
Theme and goals
The capture and analysis of rich data in collaborative learning environments using computational methods has long played an important role in developing understanding through the generation of insightful empirical findings and robust theoretical models of the complexity of learning in groups. However, applying data-intensive methods in authentic contexts to support collaborative learning is not straightforward. Addressing the challenges of data-driven live support has become a key area of focus for research agendas in Computer-Supported Collaborative Learning and Learning Sciences communities as well as other data-intensive technology-enhanced learning communities represented at the Festival of Learning. This workshop aims to bring together learning science theory with computational methods in service of providing in-the-moment support for collaborative learning. The primary goal of the workshop is to use a process of collaborative design and extended discussion to generate shared understandings of the sometimes divergent perspectives of these different communities around using analytics, adaptivity, and adaptability for supporting learning in groups. Additionally, this workshop aims to identify still unsolved challenges and future research lines in providing intelligent support for collaborative learning, laying the groundwork for joint research efforts.
Rethinking Intelligent Support for Learning in Groups (ReISLG) is the successor of a series of workshops successfully conducted in a number of conferences in previous years. The primary goal of this workshop is to
generate common understandings of the sometimes divergent perspectives of different research communities on the use of data to support collaborative learning.
These communities include the LS, CSCL, technology-enhanced learning (TEL), artificial intelligence in education (AIED/EDM), learning at scale (L@S) and learning analytics (LAK). To make advancements in this area of research, it is important to bring together diverse perspectives to help identify the challenges and areas that are feasible for joint research efforts.
Who is this workshop for?
This is a full day workshop that will be held on June 23rd. We encourage application for participation from across different research communities, including LS, CSCL, TEL, AIED, LAK, and EDM. The intended audience includes participants interested in developing analytic, adaptive, and adaptable ways to support learning in groups who are interested in opening a multidisciplinary conversation aimed at addressing the overarching challenge of integrating and coordinating the technical and pedagogical means that are required to provide support to student’s learning across different social planes.
Expected outcomes
The expected outcomes and contributions of the workshop are the following:
1. Definition of a mid-term design vision of intelligent support for learning in groups. The workshop will bring together the sub-communities within CSCL/LS, TEL, AIED, L@S, LAK and EDM with the goal of contributing and sharing expert experiences that can help guide future research and development to creates tools that can provide support for learning in groups. Particularly, we will aim to define 5-10 forward-looking visions for this design space that will be captured in a white paper and as part of a workshop report.
2. Definition of the research space. This workshop will also aim to help identify unsolved challenges and future research lines at the intersection between the learning sciences and the educational data science in support of collaborating groups and set a grounding for possible joint research efforts. This will be captured in a in a white paper and as part of a workshop report. It may seed further work leading to a journal publication.
3. Dissemination of research and design visions to the learning sciences and educational data science communities. All the materials of the workshop, including the outcomes listed above, briefing documents created before and during the workshop, and the individual contributions (submissions - see below) of the workshop participants will be made available through the workshop’s website so other members of the learning sciences and related communities can benefit and further contribute to the design and research space.