Keynote Speech

Data-Driven Approaches in Educational Inquiry to Enhance the Quality of Teaching and Learning

Ben Daniel, PhD Higher Education Development Centre, University of Otago, New Zealand

Digital learning environments are dramatically transforming the ways teachers teach, and students learn. A student learning experience, in particular, is enhanced when the design of new digital learning environments and pedagogical activities are aligned and, informed by research. Experiences from the recent pandemic suggest that educational institutions are relying more and more on online learning. Digital learning environments generate a significant amount of useful educational research data. Data in these environments come in various forms (structured, semistructured, and unstructured). As more data and ways of analysing them become available, effective design of learning and teaching will become dependent on these data. With the growing maturity in digital learning technologies, educational data and associated analytics can be harvested and analysed to reveal useful patterns to support better decisions relating to student learning and engagement, as well as the optimisation of teaching practice. Further, access to learning analytics can enable educational researchers to examine the full latitude and trajectory of a learner's experience, the artefacts they engage with during learning, and their actions as they navigate through the learning environment. Students can use analytics and associated online tools to provide lecturers with accurate and faster feedback on their teaching. However, to fully leverage the opportunities afforded by various forms of educational data and analytics, it is necessary to understand and apply data-driven approaches to educational inquiry. In this session, I will first present an overview and value of data-driven approaches to educational inquiry, stressing their various roles in enhancing the quality of teaching and learning, as well as their limitations. Second, I will provide examples of pedagogical research initiatives that involved a systematic analysis of student learning and the redesign of research methodology programme for academic staff and postgraduate students. Third, I will describe an Educational Data Science approach, which includes the development of a digital learning environment(iMethod). iMethod enables students to access various forms of online resources (e.g., text, video, and audio) on research methods. I will then show how the learning analytics harvested from iMethod was used to inform the development of analytical frameworks and tools to support student tackle the complexity of learning research methodology. In concluding the presentation, I will discuss the importance of using educational data science research design, and the challenges associated with collecting and engaging with student digital data.

Presenter’s Profile

Ben Kei Daniel, PhD is Associate Professor in Higher Education, and the Head of Department of Higher Education Development Centre at the University of Otago, New Zealand. Prof Daniel is the convenor for Educational Technology strategic initiatives and an academic member of the IT Governance Group for the University of Otago. He obtained his PhD Jointly in Educational Technology and Design, and Artificial Intelligence in Education (AIED) from the University of Saskatchewan in Canada in 2007.

His current research focuses on Big Data and Analytics in higher education. Also, he is actively researching into what constitutes "best practice teaching" in research methods. He has published over 150 peer-reviewed publications, including five books in the areas of Artificial Intelligence in Education (AIED): Big Data and Data Science, and research methodologies in higher education. His recent book: Daniel, B. K., & Harland, T. (2017). Higher Education Research Methodology: A Step-by-Step Guide to the Research Process. London: Routledge.

Ben is an international award-winning research methodologist with a vast knowledge of theoretical and practical experiences in mentoring postgraduate students and academic staff on various aspects of research methodologies (Quantitative, Qualitative, Mixed Methods and Data Science). He holds professional memberships with the International Society for Artificial Intelligence in Education (IAIED), The Institute of Electrical and Electronics Engineers (IEEE) (Learning Technologies) and the Association for the Advancement of Computers in Education (AACE).