[on behalf of Roberto Martinez]
CFP for LAK’17
The 7th International Conference on Learning Analytics & Knowledge
Understanding, Informing and Improving Learning with Data
13-17 March 2017, Simon Fraser University, Vancouver, BC, Canada
http://lak17.solaresearch.org
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Main Submission Deadline: 17 October 2016
Poster / Tech Showcase Deadline: 2 December 2016
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The 7th International Conference on Learning Analytics and Knowledge
(LAK17) returns to the coastal city of Vancouver, BC, Canada from March
13-17, 2017. The LAK17 conference is organised by the Society for Learning
Analytics Research (SOLAR) and will be hosted by Simon Fraser University
(SFU), Canada's leading community-engaged research university.
Consolidating the experience from previous LAK conferences, we extend
invitations to researchers, practitioners, administrators, government and
industry professionals interested in the field of learning analytics and
related disciplines. This annual conference provides a forum to address
critical issues and challenges confronting the education sector today. The
success of LAK arises from its transdisciplinary nature which creates a
unique intersection of cutting-edge learning technologies, educational
research and practice, and data science.
Learning Analytics is defined more by its goals to better understand and
improve learning processes using data than by a particular theory or
methods. The challenges facing learning analytics require collaborations
among a broad range of disciplinary experts to create holistic solutions
generated from differing perspectives and approaches. This year LAK17 aims
to bring more discussion and focus to these transdisciplinary efforts.
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SUBMISSIONS
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LAK17 welcomes contributions from researchers and practitioners in the
field of Learning Analytics. The conference invites the following type of
submissions:
Research Track
---Full papers
---Short papers
---Posters (Printed or Powered)
Practitioner Track
---Presentations
---Technology Showcase
Workshops and Tutorials
In addition, LAK’17 will host a Doctoral Consortium (details to be
announced soon).
See the guidelines on the website http://lak17.solaresearch.org for more
detailed information about the required content and format of each
submission.
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RELEVANT DATES
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Research Papers (Full and Short) & Practitioner Presentations
---17 October 2016: Deadline for submissions. (extensions will not be
considered)
---5 December 2016: Notification of acceptance.
---9 January 2017: Camera-ready version.
Research Posters (Printed and Powered) & Practitioner Technology Showcase
---2 December 2016: Deadline for submissions. (extensions will not be
considered)
---15 December 2016: Notification of acceptance.
---9 January 2017: Camera-ready version of poster description.
Workshops and Tutorials
---17 October 2016: Deadline for submissions. (extensions will not be
considered)
---7 November 2016: Notification of acceptance.
---9 January 2017: Camera-ready version of workshop or tutorial summary.
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TOPICS
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LAK17 seeks contributions focusing on understanding and improving learning
through analyses of data traces generated during the learning process. A
special invitation is made for papers that cross disciplinary boundaries to
provide highly innovative technological solutions grounded in learning
theory. Papers are welcome from any education context and setting: formal
and informal learning; workplace, K-12 and tertiary education; online,
distance, blended, mobile and traditional modes of learning.
The following categories represent the objectives of Learning Analytics
research and practice and will be used to classify submissions:
TRACING LEARNING
---Feature Finding: Studies that identify and explain useful data features
for analyzing understanding and optimizing learning.
---Learning Metrics: Studies that assess the learning progress through the
computation and analysis of learner actions or artefacts.
---Data Storage and Sharing: Proposals of technical and methodological
procedures to store, share and preserve learning traces.
UNDERSTANDING LEARNING
---Data-Informed Learning Theories: Proposal of new learning theories or
revisions / reinterpretations of existing theories based on large-scale
data analysis.
---Insights into Particular Learning Processes: Studies to understand
particular aspects of a learning process based on data analysis. Examples
are inquiries that analyse traces of students’ cognition, metacognition,
affect or motivation using various forms of data.
---Modeling: Creating mathematical, statistical or computational models of
a learning process, including its actors and context.
IMPROVING LEARNING
---Feedback and Decision-Support Systems: Studies that evaluate the impact
of feedback or decision-support systems based on learning analytics
(dashboards, early-alert systems, automated messages, etc.).
---Data-Informed Efforts: Empirical evidence about the effectiveness of
learning analytics implementations or educational initiatives guided by
learning analytics.
---Personalized and Adaptive Learning: Studies that evaluate the
effectiveness and impact of (semi-)automatic adaptive technologies based on
learning analytics.
META-ISSUES
---Ethics and Law: Exploration of issues and approaches to the lawful and
ethical capture and use of educational data traces and the application of
learning analytics tools and implementations
---Adoption: Discussion and evaluation of strategies to adopt learning
analytics initiatives in educational institutions
---Scalability: Discussion and evaluation of strategies to scale the
capture and analysis of information at the program, institution or national
level
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CONFERENCE ORGANIZERS
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General Chairs
-- -Alyssa Wise, Canada
-- -Phil Winne, Canada
-- -Grace Lynch, Australia
Program Chairs
-- Xavier Ochoa, Ecuador
-- Inge Molenaar, The Netherlands
--Shane Dawson (ex oficio), Australia
For more information please refer to the LAK’17 website at
http://lak17.solarsearch.org
