Abstract: This workshop on building capacity for learning analytics will engage participants in learning about data science research methods and applying those to learning analytics. The workshop will address key questions of the field. What is data science and what do we mean by learning analytics? What is the current state of the field? What are the major challenges of learning analytics ethics, data processes, methods of developing insights, and options for infrastructure? Where else in higher education leadership (beyond learning and teaching) are people using data science methods? How can data scientists in higher education build teams and engage researchers and practitioners in creating learning analytics systems? What are leaders in the field currently working on and what are some of the near-term possibilities?
This workshop will help participants understand more about data science and how researchers and institutions are enabling computational approaches across the services dimensions of higher education. For example how data analytics is being used to meet challenges in research, to support the journey of a learner from pre-university experiences, to marketing and recruitment, to enable personalized learning, adaptive curriculum and assessment resources, to create more effective teaching and to extend engagement with students to post-university, life-long learning.
One of the promises of big data in higher education is to enable a new level of evidence-based research into learning and instruction and make it possible to gain highly detailed insight into student performance and their learning trajectories as required for personalizing and adapting curriculum as well as assessment. In the new era of data-driven learning and teaching, researchers need to be equipped with an advanced set of competencies that encompass areas needed for computationally intensive research (e.g., data-management techniques for big data, working with interdisciplinary teams who understand programming languages as well as cognitive, behavioral, social and emotional perspectives on learning) and professional knowledge (including heuristics) that incline a researcher toward computational modeling when tackling complex research problems.
A special edition of the Journal of Technology, Knowledge and Learning is being formed devoted to these themes. The workshop will help participants understand the content of this special edition, and will learn why this field is rapidly emerging. Participants will leave better understanding how data science will play a role in changing the business models of teacher education and the global delivery of knowledge and learning.
Professor David C. Gibson is Director of Learning Futures at Curtin University and UNESCO Chair of Data Science in Higher Education Learning and Teaching. He has presented successful workshops at SITE in the past and presently serves on the Consultative Council.
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