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, computer vision, content analysis, sentiment analysis, immersive worlds, social network analysis and interaction analysis, new possibilities arise to study these factors in data-intensive contexts. 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
  • big data infrastructure for academic institutions and education companies  – cloud, grid, autonomic, stream, mobile, high performance computing
  • search in big data
  • artificial intelligence in big data analytics
  • uncertainty handling in big data
  • Internet of Things (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
  • scalable knowledge management
  • research methods for learning analytics

Track Program Committee