APSCE SIG on Learning Analytics and Educational Data Mining

The increasing amount of data generated in digital learning contexts provides opportunities to benefit from learning analytics and educational data mining as well as challenges related to interoperability, privacy, and pedagogical and organizational models. As a consequence, new methodologies and technological tools are necessary to analyse and make sense of these data and provide intelligent and personalized scaffolding and services to stakeholders including students, faculty/teachers and administrators, as well as parents. Pedagogical and organisational models must also be incorporated in order to take advantage of the intelligent and personalised scaffolding and services to ensure productive learning and teaching.

In addition, access to data from different sources raises a number of concerns related to commodification, data sharing and interoperability, and protection of privacy for individuals and business interests for institutions. Learning Analytics and Educational Data Mining demand a new approach to research ethics and data management. Among other things, the pedagogic use for learners have to be balanced against business and commercial interests. Usage of data for pedagogic benefit is, therefore, the primary driver for analytics. This is, as yet, an area of intensive study.

Although learning analytics and educational data mining have been gaining traction in international research communities, this is an emergent research field in Asian-Pacific Society of Computers in Education (APSCE). The objective of the Special Interest Group (SIG) is to gather researchers as well as stakeholders, including educational technologists, researchers, and practitioners who are involved in the analysis and deployment process and to increase awareness of learning analytics and educational data mining in the APSCE community.