CALL FOR PAPERS
The 16 IEEE International Conference on Advanced Learning Technologies ICALT2016
th
Austin, Texas, USA, July 25-28 2016
http://www.ask4research.info/icalt/2016/
Deadline: January 18, 2016 (23:59 GMT -10:00, Hawaii Standard Time)
Track 7 Big Data in Education and Learning Analytics (BDELA@ICALT2016)
Track Program Chairs
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Ching Sing Chai, National Institute of Education, Singapore
Hsin-Yi Chang, National Taiwan University of Science and Technology, Taiwan
Jelena Jovanovic, University of Belgrade, Serbia
Vive Kumar, Athabasca University, Canada (vivek@athabascau.ca)
Riccardo Mazza, University of Lugano, Switzerland
Abelardo Pardo, University of Sydney, Australia
Track Description and Topics of Interest:
The analysis and discovery of relations characterising human learning, and
contextual factors that influence these relations have been one of the
contemporary and critical global challenges faced by researchers in a number of
areas, particularly in Education, Psychology, Sociology, Information Systems, and
Computing. These relations typically concern learners’ achievements and the
overall learning experience, and the effectiveness of the learning context. Be it
the assessment marks distribution in a classroom context or the mined pattern of
best practices in an apprenticeship context, analysis and discovery have always
addressed the elusive causal question about the need to best serve learners’
learning efficiency, learning effectiveness, as well as the overall learning
experience, and the need to make informed choices on a learning context’s
instructional effectiveness.
Significant advances have been made in a number of areas from educational
psychology to artificial intelligence in education, which explored factors
contributing to learners’ proactive role in the learning process and instructional
effectiveness. With the advent of new technologies such as eye-tracking, activities
monitoring, video analysis, content analysis, sentiment analysis, social network
analysis and interaction analysis, one could study these factors in a data-intensive
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fashion. This very notion is what is currently being explored at the intersection of
big data and learning analytics, which includes related areas such as learning
process analytics, institutional effectiveness, academic analytics, web analytics
and information visualisation.
BDELA@ICALT2016 will explore continuous monitoring of learner progress and
traces of skills development of individual learners as well as learning groups, both
within and across programs and institutions. It will discuss issues concerning
continuous evaluation of achievements resulting from institutional educational
practices to gauge alignment with strategic plans and alignment of governmental
strategies. It will examine assessment frameworks of academic productivity to
continuously measure impact of teaching. It will discuss concerns such as quality
of instruction, attrition, and measurement of curricular outcomes using big data
and associated methods and techniques as the premise.
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big data theory, science and technology for education and learning
o analysis of unstructured and semi-structured data
o security, privacy and ethics of big data analytics
o veracity in big data
o scalability of machine learning and data mining algorithms for big data
o computing infrastructure for big data – cloud, grid, autonomic, stream,
mobile, high performance computing
o search in big data
o artificial intelligence in big data analytics
o uncertainty handling in big data
applications of big data in education and learning analytics
o detecting student’s approach to learning
o analytics in academic administration
o data analytics in complex training
o gaming analytics and sports analytics
o evidence-driven instruction in inter and individual disciplines
o big data and educational technology
o analytics in academic strategic planning
o cultural analytics
o large-scale social networks
techniques of big data in education, knowledge and learning analytics
o evidence-driven mixed-initiative learning
o data-intensive learning and instructional design
o emerging standards in learning analytics
o sentiment analysis
o large-scale productivity analysis
o big data infrastructure for academic institutions and SMEs
o scalable knowledge management
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Members of Track Program Committee
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Alejandra Martínez, University of Valladolid, Spain
Alfred Essa, McGraw-Hill Education, USA
Amal Zouaq, Royal Military College of Canada, Canada
Anastasios Economides, University of Macedonia, Greece
Ben Daniel, University of Otago, New Zealand
Christos Doulkeridis, University of Piraeus, Greece
Dragan Gasevic, University of Edinburgh, UK
Hendrik Drachsler, Open University of the Netherlands
James Willis, Indiana University, USA
José A. Pino, University of Chile
Katrien Verbert, Technische Universiteit Einhoven, Holand
Lanqin Zheng, Beijing Normal University, China
Maggie Minhong Wang, The University of Hong Kong
Mark Brown, Massey University, New Zealand
Michael Derntl, RWTH Aachen University, Germany
Mimi Recker, Utah State University, USA
Negin Mirriahi, University of New South Wales, Australia
Pierre Tchounikine, University Joseph Fourier, France
Sabine Graf, Athabasca University, Canada
Shane Dawson, University of South Australia
Simon Buckingham Shum, Open University, UK
Sridhar Ieyer, Indian Institute of Technology Bombay, India
Stefan Dietze, L3S Research Center, Germany
Vanda Luengo, University Joseph Fourier, France
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