-------- Original Message -------- Subject: [WI] Call for Papers: IEEE WCCI 2012 special session - EDM 2012 Date: Mon, 5 Dec 2011 10:22:37 +1100 From: AAG Newsletter advancedanalytics@uts.edu.au To: Benedict.Choy@uts.edu.au
From: *AAG Newsletter* <advancedanalytics@uts.edu.au mailto:advancedanalytics@uts.edu.au> Date: 30 November 2011 18:00 Subject: Call for Papers: IEEE WCCI 2012 special session - EDM 2012 To: SeminarGroup <seminar@advancedanalytics.org.au mailto:seminar@advancedanalytics.org.au>, QCIS <qcis@advancedanalytics.org.au mailto:qcis@advancedanalytics.org.au>
Apologize if you receive more than one copy.
Paper deadline: December 19, 2011
** 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 -------------------- 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) 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: Dec 19, 2011 - 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&... 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@uts.edu.au mailto:xinhua.zhu@uts.edu.au