Towards Continuity of Personalisation in a Large Blended Course

Effective teaching and learning in a large blended learning can be challenging, especially when a course population is diverse. Existing adaptive systems mostly focus on supporting students' self-regulated work at home. There also exist a few systems that help instructors make classroom more interactive. This paper present Quizitor - an online platform that is capable to deliver both the at-home and the in-class assessment. We believe, that combining these two streams of data can help achieve more accurate student modelling and potentially, more effective adaptive support in blended settings. The pilot evaluation of Quizitor demonstrates that a model aggregating data from student activity conducted at home and in class predicts students' grades better than models separately trained on either of these two types of activity.