Ghent University (BELGIUM)
About this paper:
Appears in: INTED2018 Proceedings
Publication year: 2018
Pages: 4508-4516
ISBN: 978-84-697-9480-7
ISSN: 2340-1079
doi: 10.21125/inted.2018.0879
Conference name: 12th International Technology, Education and Development Conference
Dates: 5-7 March, 2018
Location: Valencia, Spain
Although blended learning has many opportunities for flexible learning, it also includes challenges. One of the major challenges is to keep students motivated. An opportunity often overlooked by educational scientists is the vast amount of data generated by learning management systems, or summarised in one word: Learning Analytics (LA). LA could be used to promote students’ motivation by providing teachers insight into students’ learning activities within a blended learning context. However, this research field is still underdeveloped. Little is known about students’ perceptions of LA, but this information is fundamental to design supportive LA interventions. Therefore, in a first phase, this study aims to investigate how motivated student teachers are in a blended learning context. Following self-determination theory, this study examines whether the student teachers’ basic needs are fulfilled. In a subsequent second phase, the student teachers’ perceptions of a hypothetical use of LA are explored. In December 2016, a study was conducted in a blended learning setting in teacher education. The results indicate that student teachers’ basic needs are not fulfilled. The second major finding suggests that student teachers have different opinions about the use of LA, and overall that the majority of the student teachers is not convinced about the added value. Yet, these perceptions are based on a hypothetical use of LA and not on student teachers’ own experiences. Future research should investigate what student teachers’ perceptions are after a LA intervention is conducted in the blended learning setting, and whether this intervention can give appropriate input to the teacher enabling him or her to better accommodate students’ basic needs.
Learning Analytics, Blended Learning, Self-Determination Theory.