IEEE Task Force of Educational Data Mining

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.

We propose this special session as a hybrid one with WCCI 2012, because the Educational Data Mining and its corresponding area Learning Analytics involve topics of interest relevant to IJCNN, CEC and FUZZY-IEEE. A hybrid special session would mostly benefit the WCCI community.

Keywords

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

Important Dates: