LAK12: 2nd International Conference on Learning Analytics & Knowledge
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 outcomes.
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 groups)
• 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 analytics
• 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 analytics
• 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’ position statements.
• 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 submissions.
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.