The multi-disciplinary research approach of Learning Analytics (LA) has provided methods to understand learning and teaching process by analyzing logs collected during diverse teaching-learning activities and potentially enrich such experiences. This talk will propose the Learning Evidence Analytics Framework (LEAF) and draw a research road-map of an educational big data-informed evidence-based education system. It focuses on the approach of developing novel techniques by applying the knowledge base of LA that can help to extract evidence of effective teaching-learning practices. Finally, it shows that teachers can refine their instructional practices, learners can enhance learning experiences, and researchers can study on the dynamics of the teaching-learning cases with LEAF.
Hiroaki Ogata is a Professor at the Academic Center for Computing and Media Studies, and the Graduate School of Informatics at Kyoto University, Japan. His research includes Computer Supported Ubiquitous and Mobile Learning, CSCL, CALL, and Learning Analytics. He has published more than 300 peer-reviewed papers including SSCI Journals and international conferences. He received the APSCE Distinguished Researcher Award in 2014 and several Best Paper Awards. Also he has given keynote lectures in several countries. He is an associate editor of the IEEE Transactions on Learning Technologies, RPTEL, and IJMLO, and also an editorial board member of IJCSCL, IJAIED, JLA and SLE. He is an Executive Committee member of SOLAR and APSCE.