[Elsnet-list] EDM2012 - Paper submission deadline extended to Jan 18, 2012!

AAG Newsletter advancedanalytics at uts.edu.au
Thu Dec 15 00:44:58 CET 2011


--------------------

EXTENDED DEADLINE!!!

--------------------



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**



=====================================



Call For Papers

--------------------

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-2012



Held in conjunction with

The 2012 IEEE Congress on Computational Intelligence (WCCI 2012)

http://www.ieee-wcci2012.org/

June 10 - 15, 2012

International Convention Centre, Brisbane, Australia





Important dates: (please closely check the WCCI 2012 website for the
possible update)

----------------------

- Paper submission deadline:                          Jan 18, 2012

- Paper acceptance notification date:              Feb 20, 2012

- Final paper submission deadline:                  Apr 2, 2012





Introduction:

------------

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:

------------------

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:

------------------------

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&view=article&id=58&Itemid=67





Paper review and publication:

-----------------------------

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:

--------------------------

- Longbing Cao, University of Technology Sydney, Australia

- Nitesh Chawla, University of Notre Dame, USA

- George Siemens, Athabasca University, Canada





Session Organizing Chairs:

--------------------------

- Xinhua Zhu, University of Technology Sydney, Australia

- Helen Lu, University of Technology Sydney, Australia



Supported by:

-------------

IEEE Task Force on Educational Data Mining

http://datamining.it.uts.edu.au/edd/





Contact:

--------

Dr Xinhua Zhu

Email: xinhua.zhu at uts.edu.au
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