Big Data and Learning Analytics in Higher Education

Dr Ben Daniel is a Senior Lecturer in Higher Education, and heads the Educational Technology Group at the University of Otago—New Zealand. In 2007 he completed his Ph.D. jointly in Computer Science and Educational Technology at the University of Saskatchewan in Canada. Previously, Dr Daniel was a Lecturer and a Senior Health Research and Innovation Analyst with the Office of Associate Vice President Research-Health (University of Saskatchewan)/Vice President Research and Innovation (Saskatoon Health Region)—Saskatoon, Canada. For over a decade Dr Daniel taught undergraduate, postgraduate students, academic staff, and Senior Corporate Executives on Research Methodologies; Program Evaluation and Logic Modelling; Quality Improvement, Statistics and Biostatistics as well as Advanced Learning Technologies. An active researcher, Dr Daniel has published over 50 peer-reviewed publications (2 edited books, 2 authored book, 13 book chapters, 10 peer-reviewed journal articles, 26 peer-reviewed conference papers, 10 peer-reviewed workshops and posters, and over 10 technical reports).  His current research is focused generally on exploring the value of Big Data and learning analytics in influencing learning, teaching and research in the context of higher education. He is also investigating theories and praxis of teaching research methodologies in Higher Education and the corporate sector.

• ​Theory and Practice.- Global challenges in higher education.- Technological trends in higher education.- Data Science.- Big data in higher education.- Learning analytics.- Big Data Platforms and Systems.- Analytical platforms.- Systems.- Databases.- Tools.- Visualization.- Dashboards.- Measurement and Methodologies.- Measures, indicators, metrics.- Data mining techniques.- Data capture.- Data tracking.- Metadata.- Methodologies.- Institutional Best Practices.- Case studies/best practices.- Polyicy implication on learning, teaching, and research.- Challenges and opportunities.- Future Trends.- Lessons learned.- Future perspectives in big data.- Conclusions.