Track Program Chairs

Track description and topics of interest

The analysis and discovery of relations characterizing 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 focus on learners’ achievements and the overall learning experience, and the effectiveness of learning environments. Be it the assessment mark distribution in a classroom context or the mined patterns 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 quality of the learning experience, and the need to make informed choices on improving learning environments.

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, activity monitoring, video analysis, content analysis, sentiment analysis, immersive worlds, social network analysis and interaction analysis, new possibilities arise to study these factors in a data-intensive context. 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, text/web analytics and information visualization.

BDELA explores monitoring of learner progress and tracing of skill development of individual learners as well as learning groups, both within and across programs and institutions. It will discuss issues concerning evaluation of achievements resulting from institutional educational practices to gauge alignment with strategic plans at different levels. It will examine assessment frameworks of academic productivity to 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.

Topics include but are not limited to:

• Big data theory, science and technology for education and learning
  • security, privacy, inclusivity, fairness and ethics of big data analytics
  • veracity in big data
  • scalability of machine learning and data mining algorithms for big data
  • computing infrastructure for big data – cloud, grid, autonomic, stream, mobile, high performance computing
  • search in big data
  • artificial intelligence in big data analytics
  • uncertainty handling in big data
  • IoT and big data analytics
• Applications of big data in education and learning analytics
  • detecting student’s approach to learning
  • analytics in academic administration
  • data-informed learning and instructional design
  • gaming analytics and sports analytics
  • evidence-driven instruction in inter and individual disciplines
  • analytics in academic strategic planning
  • cultural analytics
  • large-scale social networks
  • educational data literacy
  • technological literacy and analytics
  • human literacy and analytics
• Techniques of big data in education, knowledge and learning analytics
  • emerging standards in learning analytics
  • analysis of unstructured and semi-structured data
  • sentiment analysis
  • social network analysis
  • multimodal learning analytics
  • large-scale productivity analysis
  • big data infrastructure for academic institutions and SMEs
  • scalable knowledge management
  • observational research methods for analytics

Track Program Committee

Xiao HU, The University of Hong Kong, China

Dirk IFENTHALER, University of Mannheim, Germany & Curtin University, Australia, Germany

Jelena JOVANOVIC, University of Belgrade, Serbia

Bernardo Pereira NUNES, The Australian National University, Brazil

Abelardo PARDO, The University of Sydney, Australia

Demetrios G SAMPSON, University of Piraeus, Greece

Guanliang CHEN, Monash University, Australia

Christy W.L. CHEONG, Macao Polytechnic Institute, Macao S. A. R.

Daniele DI MITRI, DIPF | Leibniz Institute for Research and Information in Education, Germany

Shihui FENG, The University of Hong Kong, Hong Kong S. A. R.

Rafael FERREIRA, Federal Rural Univerisity of Pernambuco, Brazil

Yoshiko GODA, Kumamoto University, Japan

Hussein HARUNA, Tecnológico de Monterrey, Mexico

Anna HUANG, National Central Univerity, Taiwan

Chester HUANG, National Kaohsiung University of Science and Technology, Taiwan

Vitomir KOVANOVIC, The University of South Australia, Australia

Leon LEI, The University of Hong Kong, Hong Kong S. A. R.

Feng LIN, Singapore University of Social Sciences, Singapore

Danny Y.T. LIU, The University of Sydney, Australia

Meijun LIU, Fudan University, China

Wannisa MATCHA, Prince of Songkla University, Thailand

Tatsunori MATSUI, Waseda University, Japan

Negin MIRRIAHI, University of South Australia, Australia

Andy NGUYEN, University of Oulu, Finland

Thiago PROCACI, Federal University of the State of Rio de Janeiro, Brazil

Chen QIAO, The University of Hong Kong, Hong Kong S. A. R.

Muhittin ŞAHIN, University of Mannheim Ege University, Germany

Clara SCHUMACHER, Humboldt Universität zu Berlin, Germany

Yuan SUN, National Institute of Informatics, Japan

Hengtao TANG, University of South Carolina, USA

Richard TORTORELLA, University of North Texas, USA

Elle Yuan WANG, Arizona State University, USA

Albert YANG, Kyoto University, Japan

Christopher YANG, Kyoto University, Japan

Tzu Chi YANG, Academia Sinica, Taiwan

Minghua YU, East China Normal University, China