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LAK12: 2nd International Conference on Learning Analytics & Knowledge
Vancouver, 29 April – 2 May 2012
KATY BÖRNER is the Victor H. Yngve Professor of Information Science at the
School of Library and Information Science
GEORGE SIEMENS researcher and strategist with the Technology Enhanced
Knowledge Research Institute at Athabasca University in Alberta, Canada
BARRY WELLMAN S.D. Clark Professor at the Department of Sociology,
University of Toronto, Director of NetLab.
Challenges & Opportunities
We are experiencing an unprecedented explosion in the quantity and quality
of information available not only to us, but about us. We must adapt
individually, institutionally and culturally to the transition in technologies and
social norms that makes this possible, and question their impacts. What are
the implications of such data availability for learning and knowledge building
— not only in established contexts, but also in the emerging landscape of
free, open, social learning online?
Within the learning technologies research and development community, this
question has catalyzed the International Learning Analytics & Knowledge
Conference, now in its second year. Learning Analytics is concerned with the
collection, analysis and reporting of data about learning in a range of
contexts, including informal learning, academic institutions, and the
workplace. It informs and provides input for action to support and enhance
learning experiences, and the success of learners. Learning and Knowledge
Analytics 2012 supports the emerging academic field by connecting the
community of researchers and developers, creating and disseminating new
developments and practices, studying transformations, and providing
ongoing evaluation and critique of the conceptual, technical, and practice
Educational institutions are under intense pressure to make improvements
and savings, while still delivering on their mission to support learners using
all possible means. The effective use of information about learners can be
part of the solution to this dilemma. Analytics seeks to provide rapid, real
time answers to questions such as:
• Who is at risk of failing?
• What kinds of interventions make most difference to learners?
• How am I doing compared to my peers?
• How effective is this course?
• Who are the key people in this community?
• Are there quality learning conversations in this forum?
• What is or can be different about learning and learning experiences
when combined with learning analytics?
Social media, open data, web analytics, semantic web, data mining and
recommendation engines may hold the answers, but they also combine to
create a powerful but complex data deluge, which surpasses the ability of
organizations to make sense of it. What is needed to tame this technical
complexity for learners, educators and administrators?
While ‘business intelligence’ infrastructure is well established for certain kinds
of performance indicator, is there an equivalent for tracking the rather more
complex processes of authentic learning and knowledge sharing? Is there the
risk that learning analytics will damage learning and knowledge flows by
monitoring and measuring them inappropriately?
These technical, pedagogical, policy and social domains must be brought into
dialogue with each other to ensure that interventions and organizational
systems serve the needs of all stakeholders.
We invite submissions on topics including but not limited to:
Conceptual & Empirical
• Connections between learning analytics and the learning sciences (e.g.,
self-regulated learning, critical thinking, sense making and learning analytics)
• New models of learning enabled by analytics
• Educational research methods and learning analytics
• Learning analytics in relationship to other fields (e.g., institutional
analytics; educational data mining)
• Communicating analytics (e.g., data selection, display, visualization, user
• Ethical considerations (e.g., privacy and ownership)
• Learner modeling
• The influence of analytics on designing for learning
• The influence of analytics on delivery and support of learning
• The study of emotion, flow, and affective data in learning analytics
• Validating analytics empirically
• The limits of web analytics
• Social network analysis
• Cross-platform and cloud learning analytics
• Learning environments that capture different kinds of data
• Software development and use in analytics
• The role of knowledge representation and ontologies in learning
• The semantic web and linked data: meaning in connections
• Data mining in learning analytics
• Artificial intelligence in learning analytics
• Internet of things (sensors) and learning applications
• “Big Data” applications and opportunities in learning and education
• Latent semantic analysis/natural language processing
• Attention metadata
• Architecture of learning environments and implications to learning
• Visualization: data, learner networks, conceptual knowledge
• Predictive applications of data
• Interventions based on analytics
• Social and technical systems to manage information abundance
• Personalization and adaptivity in the learning process
• Corporate and higher education case studies of learning analytics
• Learning analytics for intelligent tutoring systems
• Open data: data access for learners
• Harmonizing individual learning with organizational learning
• Organizational learning and knowledge sharing models
• Importing insights for existing analytics
• Use of learning analytics in centralized (learning management systems)
and decentralized (personal learning environments) settings
• Planning, deploying, and evaluating enterprise-wide learning analytics
The following types of submission are invited:
• Full Papers: Use a full paper to share substantive conceptual, technical
and empirical contributions. 10 pages max.
• Short Papers: Use a short paper to share preliminary conceptual,
technical and empirical contributions. 4 pages max.
• Design Briefing: Do you spend more time building learning analytics
tools than writing about them? Specifically with people like interface
designers, system architects and programmers in mind, use a briefing to
share a design concept, tool or challenge. 4 pages max.
• Demonstrations: A carefully planned, live demonstration of a tool is the
most engaging and informative way to show interactive software, ranging
from early prototype to robust product. 1-2 page abstract, clarifying the
maturity of the tool, including at least one link to a current demo movie.
• Panels: Panels provide the chance for delegates to hear a range of
speakers air a topical issue, e.g. diverse approaches to a problem, or a debate
on a hot topic. 2 pages max, including the names of confirmed panellists. The
final paper from the Panel’s chair may be up to 4 pages, including panellists’
• Workshops: Workshops (April 29, 2012) provide the opportunity to
explore learning theory, analytics, methods and tools in depth. Workshops
should be designed to be interactive and may reflect for example,
compilations of short and/or enlightening presentations, demonstrations, and
instructional workshops. The length of the Workshop sessions can be a half or
full day allowing for sets of interactive activities for experience sharing and
brainstorming. Please use the workshop/tutorial template, outlining the
significance of the topic, the workshop format, and your track record.
• Tutorials: Tutorials (also April 29, 2012) provide the chance to take
participants deep into a specific tool or technique in which you are
experienced, or an introduction to a topic/class of tools. This could be as
short as 1 hour, to a half day. Please use the workshop/tutorial template for
Full and Short Papers, Design Briefings, and the abstracts for Demonstrations
and Panels will be published in the main proceedings.
Submission and Publication
LAK2011 proceedings will be published in the ACM Digital Library
International Conference Proceedings Series, and we expect 2012 to follow.
Author guidelines on the website.
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