Abstract
Learning analytics can be used at various levels of granularity to examine and visualize learning paths of students and groups. Learning analytics (LA) refers to the measurement, collection, analysis and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which learning occurs.
Research and development carried out in two projects will be presented to exemplify LA application to students’ study paths and competence development needs. Particularly, the research examines the guidance of 1st and 2nd students as a major development challenge at institutions, to identifying early indicators to be addressed in guidance interaction. At the institutional level, analytics may help to identify needed guidance or refinement of curricula or services to support fluent study paths.
Further, artificial intelligence methods which combine register-based and activity-based data in applications and advanced feedback designs can make the progress in studies visible to involved actors. What takes place between data being collected and presented back to the end-users is critical to maintain the transparency of analytical procedures and algorithm use, i.e., what is actually being represented by the visualizations. Ethical, legal and data protection issues play an important role in the definition of practices and standards for use of analytics in institutions.
About Hanni Muukkonen
Hanni Muukkonen, PhD, professor in Educational Psychology at the University of Oulu, Faculty of Education. She has carried out reseach on collaborative learning and knowledge creation, technology-enhanced learning and learning analytics. Her interests include inquiry learning, competence development as well as methodological development and application particularly in higher education but also vocational education and upper secondary education. Current research projects include AnalyticsAI, CompLeap and Supporting together!