Combining observational and experiential data to inform the redesign of learning activities

A main goal for learning analytics is to inform the design of a learning experience to improve its quality. The increasing presence of solutions based on big data has even questioned the validity of current scientific methods. Is this going to happen in the area of learning analytics? In this paper we postulate that if changes are driven solely by a digital footprint, there is a risk of focusing only on factors that are directly connected to numeric methods. However, if the changes are complemented with an understanding about how students approach their learning, the quality of the evidence used in the redesign is significantly increased. This reasoning is illustrated with a case study in which an initial set of activities for a first year engineering course were shaped based only on the student's digital footprint. These activities were significantly modified after collecting qualitative data about the students approach to learning. We conclude the paper arguing that the interpretation of the meaning of learning analytics is improved when combined with qualitative data which reveals how and why students engaged with the learning tasks in qualitatively different ways, which together provide a more informed basis for designing learning activities.