Track 6. Big Data in Education and Learning Analytics (BDELA)

Track Program Chairs

Vive Kumar

Athabasca University, Canada
(CO-ORDINATOR)

Abelardo Pardo

The University of Sydney, Australia

Dirk Ifenthaler

University of Mannheim, Germany & Curtin University, Australia

Xiao Hu

The University of Honk Kong, Hong Kong

Demetrios G Sampson

University of Piraeus, Greece & Curtin University, Australia

Stephen Yang

National Central University, Taiwan

Bernardo Pereira

Australian National University, Australia

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 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, activity monitoring, video analysis, content analysis, sentiment analysis, immersive worlds, social network analysis and interaction analysis, one could 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, web analytics and information visualization.

BDELA will explore 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 and alignment of governmental strategies. 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:

  • Big data theory, science and technology for education and learning

    • analysis of unstructured and semi-structured data
    • security, privacy 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 analytics in complex training
    • gaming analytics and sports analytics
    • evidence-driven instruction in inter and individual disciplines
    • big data and educational technology
    • analytics in academic strategic planning
    • cultural analytics
    • large-scale social networks
    • data literacy
    • technological literacy and analytics
    • human literacy and analytics
  • Techniques of big data in education, knowledge and learning analytics

    • evidence-driven mixed-initiative learning
    • data-intensive learning and instructional design
    • emerging standards in learning analytics
    • sentiment analysis
    • large-scale productivity analysis
    • big data infrastructure for academic institutions and SMEs
    • scalable knowledge management
    • observational research methods for analytics
Accept Cookies