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Subject:
From:
Joseph Jay Williams <[log in to unmask]>
Reply To:
Joseph Jay Williams <[log in to unmask]>
Date:
Tue, 4 Oct 2016 15:33:23 -0400
Content-Type:
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This is a nice connection between recent work on MOOCS and learning at
scale (like the new ACM Learning @ Scale conference
<http://learningatscale.acm.org/las2017/>, proceedings due Oct 25) – and
the existing literature within educational technology at conferences like
AIED (http://iaied.org/journal/about/).

** Special Issue Associate Editors
Daniel M. Russell, Google, Inc., USA
Ido Roll, University of British Columbia, Canada
Dragan Gasevic, University of Edinburgh, UK

** Important Dates
Submission of complete manuscript December 11, 2016
Joseph


Joseph Jay Williams, PhD
www.josephjaywilliams.com: Homepage and Resources on Online Education
www.mooclets.com: Resources on using MOOClets to Adaptively Personalize
Online Education through Machine Learning and Crowdsourcing

Research Fellow
Vice Provost for Advances in Learning (VPAL) Research Team
Harvard University





Dear Colleague,

We invite you to submit your paper for a special issue of the International
Journal of Artificial Intelligence in Education (
www.springer.com/computer/ai/journal/40593
<http://orange.hosting.lsoft.com/trk/click?ref=znwrbbrs9_6-1159dx3ff6bx0259&>
).



*Learning at Scale: What Works & Lessons Learned*
A special issue of the International Journal of Artificial Intelligence in
Education

www.springer.com/computer/ai/journal/40593
<http://orange.hosting.lsoft.com/trk/click?ref=znwrbbrs9_6-1159dx3ff6cx0259&>



The annual Learning at Scale conference series (learningatscale.acm.org/
las2016/
<http://orange.hosting.lsoft.com/trk/click?ref=znwrbbrs9_6-1159dx3ff6dx0259&>)
has
created a track record of high quality papers about the practices, methods,
and science for teaching large numbers of students. This special issue will
consolidate what we have found that works well in terms of pedagogy,
technology, analytics, and theory. Overall, this special issue will make a
contribution to the science and practice of large online learning/teaching
systems.

For this special issue we solicit paper submissions reporting on rigorous
research on methodologies, studies, analyses, tools, pedagogies, or
technologies for learning at scale. Learning at Scale includes MOOCs, games
(including massively multiplayer online games), citizen science
communities, and other types of learning environments which (a)
provide learning experiences to large number of learners and/or (b) produce
detailed, high volume data about the learning process.

Papers that tackle specific aspects of scale are particularly encouraged,
for example, papers that deal with learning or educational phenomena that
can only occur, be supported, or be observed with very large numbers of
students, or in which the system improves after being exposed to data from
previous use by many students.



** Special Issue Associate Editors
Daniel M. Russell, Google, Inc., USA
Ido Roll, University of British Columbia, Canada
Dragan Gasevic, University of Edinburgh, UK



** Important Dates
Submission of complete manuscript December 11, 2016
Reviews sent to authors  February 11, 2017
Revised manuscript due  April 16, 2017
Reviews sent to authors  June 16, 2017
Final version of manuscript due August 16, 2017

Publication of Special Issue
Each paper will appear on Online First as soon as it has been accepted and
processed.
The full Special Issue will be assembled in the third quarter of 2017.


** Submission instructions
Submit papers at aied.edmgr.com/
<http://orange.hosting.lsoft.com/trk/click?ref=znwrbbrs9_6-1159dx3ff6ex0259&>
using
the special submission type: "SI L@S”.


** Topics of Interest
The scope includes (but is not limited to) the topics:

• Usability studies and effectiveness studies of design elements for
students or instructors, including:

• Status indicators of student progress

• Status indicators of instructor effectiveness

• Tools and pedagogy to promote community, support learning, or
increase retention in at-scale environments

• Analysis of log data about student behavior, e.g.:

• Assessing reasons for student outcome as determined by modifying tool
design

• Modeling students based on responses to variations in tool design

• Evaluation strategies such as quizzes or discussion forum design

• Instrumenting systems and data representation to capture relevant
indicators of learning.

• Personalization and adaptation, based on log data, user modeling, or
choice.

• Studies of applications of existing and newly developed learning theories
to the MOOC context (peer learning, project based learning, etc.).

• Large online learning in the developing world

• New tools and techniques for learning at scale, including:

• Games for learning at scale

• Automated feedback tools (for essay writing, programming, etc)

• Automated grading tools

• Tools for interactive tutoring

• Tools for learner modeling

• Interfaces for harnessing learning data at scale

• Innovations in platforms for supporting learning at scale

• Tools to support for capturing, managing learning data

• Tools and techniques for managing privacy of learning data

• Investigation of observable student behaviors and their correlation with
learning, e.g.:

• What do more successful learners do more of?

• What do more successful instructors do more of?

• Self- and co-regulation of learning at scale

• Collaborative learning in courses that have scale

• Depth and retention of learning and understanding

• Improvements to learning, community, and pedagogy in large-scale
in-person and blended online and in-person courses

• Instructional principles for learning at scale

• Facilitation of informal subcommunities

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