Learning analytics and open, flexible, and distance learning

ABSTRACT

This article serves as a reflection on the papers accepted for this special issue. Although 2011 saw the first learning analytics conference, the field draws from a long history of research in associated disciplines such as psychology, statistics, education and computer science. However, the novelty of learning analytics research stems from the access and analysis of large, yet granular, data sources that are generated from student interactions in online activities. The vast array and volume of accessible learning data have promulgated new approaches to understanding and measuring learning. As researchers pursue such algorithmically generated insights, there are parallel challenges to individual ethics and privacy and a broader consideration of the impact of an increased quantification of education. This special issue on the role of learning analytics in open, flexible, and distance learning (OFDL) environments aims to bring alternate perspectives to bear on how such analyses are improving learning while still addressing social and cultural challenges.

KEYWORDS:

• learning analytics
• open, flexible and distance learning
• data
• ethics

Additional information

Notes on contributors

George Siemens

George Siemens researches technology, networks, analytics, and openness in education. Dr. Siemens is Professor of Psychology at University of Texas, Arlington. He co-leads the development of the Center for Change and Complexity in Learning at University of South Australia.