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Roberto Martinez <[log in to unmask]>
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Roberto Martinez <[log in to unmask]>
Tue, 20 Sep 2016 10:38:34 +1000
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3nd 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     #lak17


Main Submission Deadline: 17 October 2016

Poster / Tech Showcase Deadline: 2 December 2016

Doctoral Consortium Deadline: 10 November 2016


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.




LAK17 welcomes contributions from researchers and practitioners in the
field of Learning Analytics. The conference invites the following type of

Research Track

---Full papers

---Short papers

---Posters (Printed or Powered)

Practitioner Track


---Technology Showcase

Workshops and Tutorials

Doctoral Consortium

See the guidelines on the website for more
detailed information about the required content and format of each




Research Papers (Full and Short) &  Practitioner Presentations

---17 October 2016: Deadline for submissions.  (extensions will not be

---5 December 2016: Notification of acceptance.

---9 January 2017: Camera-ready version.

Workshops and Tutorials

---17 October 2016: Deadline for submissions.  (extensions will not be

---7 November 2016: Notification of acceptance.

---9 January 2017: Camera-ready version of workshop or tutorial summary.

Doctoral Consortium

---10 November 2016: Deadline for submissions
---3 January 2017: Notification of acceptance

Research Posters (Printed and Powered) & Practitioner Technology Showcase

---2 December 2016: Deadline for submissions.  (extensions will not be

---15 December 2016: Notification of acceptance.

---9 January 2017: Camera-ready version of poster description.




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:


---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.


---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.


---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.


---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




General Chairs
-- -Alyssa Wise, Canada
-- -Phil Winne, Canada

-- -Grace Lynch, Australia

Program Chairs
-- Xavier Ochoa, Ecuador
-- Inge Molenaar, The Netherlands

--Shane Dawson (ex officio), Australia

For more information please refer to the LAK’17 website at

UTS CRICOS Provider Code: 00099F
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