Evidence-Based Learning Design Through Learning Analytics

Abstract

Learning Management Systems (LMS) are widely used to support teaching, produce large datasets as instructors upload content, post assignments and tests, and feedback to learners. While using content provided, submitting assignments, etc., students also create large amounts of data, which are potentially valuable for assessing engagement levels. Issues of measuring student engagement and its contribution to success have led to the launch of a new field of study, Learning Analytics. LMSs with analytics tools have potential as an evidence-based systematic approach to evaluating and improving education. This approach can predict student dropout and retention, and provide data for institutional strategy-making processes, but other benefits are less well established. The possible impact of such tools on course design in particular needs clarification. This study was conducted at a private foundation university in Turkey, where Blackboard LMS has been widely adopted. Data from Blackboard Analytics was used to identify the course archetypes to improve learning design. This study aims to investigate the degree of agreement between instructors’ opinion on their course design archetype and the archetype provided by Blackboard Analytics, and to identify any similarities in tool use between the local institution data and the data used in the study where course design archetypes were first identified and introduced. Mixed method sequential explanatory design was used in the analysis, and the results showed a low level of consistency between instructors’ views and the analytics findings, which is discussed in depth in this chapter. The study potentially contributes to the field of learning analytics by providing guidance on learning design, and also informing evidence-based educational decision-making, ultimately promoting student learning and improving the learning and teaching culture in higher education institutions.