Learning analytics seeks to enhance the learning process through systematic measurements of learning related data, and informing learners and teachers of the results of these measurements, so as to support the control of the learning process. Learning analytics has various sources of information, two main types being intentional and learner activity related metadata. This contribution discusses the potential to apply Buckingham and Deakin’s theoretical framework of a learning analytics infrastructure that combines learning dispositions data with data extracted from computer based, formative assessments. In a large introductory statistics course based on the principles of blended learning, combining face-to-face problem-based learning sessions with technology enhanced education, we demonstrate that students learning choices can profit from providing students with feedback based on learning analytics, as to optimize individual learning.
(Thursday 17th, 15:45-17:15) In session C14C
Dirk Tempelaar (The Netherlands)