-------- Original Message -------- Subject: [WI] EDM2012 - Paper submission deadline extended to Jan 18, 2012! Date: Wed, 21 Dec 2011 10:29:09 +1100 From: AAG Newsletter advancedanalytics@uts.edu.au To: undisclosed-recipients:;
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EXTENDED DEADLINE!!!
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Apologize if you receive more than one copy.
Paper deadline is extended to Jan 18, 2012
** Accepted papers will be published in the main conference proceedings of IEEE WCCI 2012 **
** Submission guideline and the submission system, please refer to WCCI 2012 website**
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Call For Papers
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The Hybrid Special Session on Educational Data Mining (EDM-12)
(EDM-12 is a hybrid session with IJCNN2012, FUZZY-IEEE2012, and CEC2012)
http://datamining.it.uts.edu.au/edd/index.php/edm-special-session-with-wcci-...
Held in conjunction with
The 2012 IEEE Congress on Computational Intelligence (WCCI 2012)
June 10 - 15, 2012
International Convention Centre, Brisbane, Australia
Important dates: (please closely check the WCCI 2012 website for the possible update)
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- Paper submission deadline: Jan 18, 2012
- Paper acceptance notification date: Feb 20, 2012
- Final paper submission deadline: Apr 2, 2012
Introduction:
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Educational Data comes from educational settings, e.g. interactive learning environments (multiple choice questions, response time), computer aided collaborative learning (online
learning data), and administrative data (demographics, enrolment). It has the following typical characteristics: multiple levels of meaningful hierarchy (subject, assignment, and
question levels), time, sequence, context (a particular student in a particular class encountering a particular question in a particular problem on a particular computer at a
particular time on a particular date), fine-grained (record data at different resolutions to facilitate different analyses, e.g. record data every 10s) and longitudinal (large data
recorded for many sessions for a long period of time, e.g. spanning semester land year long courses).
Educational Data Mining (EDM), a newly emerging inter-disciplinary research field in the discipline of computational intelligence, focuses on Knowledge
Discovery and Data Mining techniques to analyse data from educational settings, including interactive learning systems, intelligent tutoring systems and institutional administration
data. The primary goal of EDM is to uncover scientific evidence or patterns that are useful to gain insights and explain educational phenomena. To meet the emerging research interest in educational data mining and learning analytics, this Special Session on Educational Data Mining jointly with 2012 IEEE World Congress on Computational Intelligence
(WCCI2012) provides a leading forum for researchers to publish high quality original research papers with various topics in educational data mining and learning analytics. The topics of this special session
may include (but not limited to) cohort analysis, attribution analysis, pathway analysis, student modelling, learning and teaching behaviour analysis, learning emotion analysis,
educational psychology analysis, student performance prediction, e-learning and learning management system building, learning personalization and recommendation, learning
visualization and analysis, social network analysis in educational environment, and coursework construction.
Topics of Interest:
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The EDM Special Session provides a premier forum for sharing research and engineering results, as well as potential challenges and prospects encountered in the communities of educational data mining and learning analytics. The EDM Special Session welcomes theoretical work and applied dissemination on, but not limited to:
* Educational data processing and representation
Educational data acquisition
Educational domain representation
Educational data preparation
Educational data quality issues
TL behavior construction
EDM benchmark data
* Educational analysis
Student cohort analysis
Student attribution analysis
Pathway analysis
Student modelling
Learning and teaching behaviour analysis
Learning emotion analysis
Educational psychology analysis
Student performance prediction
Learning and learning management system building
Learning personalization and recommendation
Learning visualization and analysis
Social network analysis in educational environment, and
Coursework and curriculum construction based on learning outcomes
* TL behavior analysis
TL behavior modeling
TL behavior pattern analysis
TL demographic analysis
Replication analysis
Plagiarism analysis
TL group analysis
TL sequence analysis
TL evolution analysis
TL history analysis
Mobility analysis
* EDM social analysis
Educational social factor analysis
Educational psychological factor analysis
Educational pedagogical analysis
TL hidden network and its behavior
* Performance, effect and impact analysis
TL performance profiling
TL cause effect analysis
TL intervention evaluation
Student at academic risk scoring
* Evaluation and validation
EDM evaluation methods
TL validation methods
* EDM software and applications
EDM software and tools
Mobile computing EDM tools
Educational teacher support
Web-based EDM tools
Applications
Lessons
Submission Instructions:
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Please follow the WCCI 2012 paper formatting guide.
Papers are to be submitted through the WCCI 2012 submission system:
http://www.ieee-wcci2012.org/ieee-wcci2012/index.php?option=com_content&... http://www.ieee-wcci2012.org/ieee-wcci2012/index.php?option=com_content&view=article&id=58&Itemid=67
Paper review and publication:
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All submissions will be reviewed by following the WCCI 2012 review process. Accepted papers will be included in the WCCI 2012 main conference proceedings.
Special Session Co-Chairs:
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- Longbing Cao, University of Technology Sydney, Australia
- Nitesh Chawla, University of Notre Dame, USA
- George Siemens, Athabasca University, Canada
Session Organizing Chairs:
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- Xinhua Zhu, University of Technology Sydney, Australia
- Helen Lu, University of Technology Sydney, Australia
Supported by:
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IEEE Task Force on Educational Data Mining
http://datamining.it.uts.edu.au/edd/
Contact:
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Dr Xinhua Zhu
Email: xinhua.zhu@uts.edu.au mailto:xinhua.zhu@uts.edu.au