Learning Analytics and Learning Design

The way teachers design courses directly impact student behaviour and success

Ground-breaking research at the Open University/Institute of Educational Technology has found that the way teachers design blended and online courses significantly influences students’ engagement, satisfaction and success. For example, investigations within this research programme has identified four common patterns of learning activities of how Open University (OU) teachers have developed distance learning modules. Perhaps surprisingly given the strong standardisation processes at the OU, we found four relatively distinct clusters of learning design: constructivist, assessment-driven, balanced-variety, and social constructivist. 

Learning Design is described as a methodology for enabling teachers/designers to make more informed decisions in how they go about designing learning activities and interventions, which is pedagogically informed and makes effective use of appropriate resources and technologies. In other words, learning design is focused on ‘what students do’ as part of their learning, rather than on ‘what teachers do’ or on what will be taught. Within the OU, there is an increased recognition that learning design is an essential driver for learning.

Recent technological developments have allowed learning analytics researchers to capture the digital traces of learning activities of students and teachers in Virtual Learning Environments (VLEs). This rich and fine-grained data about actual learner behaviours offer educators potentially valuable insights into how students react to different learning designs. However, despite substantial progress in transferring learning design from implicit to explicit, there remains a paucity of evidence for how learners respond to different learning design. 

For example, recent empirical research in this programme on learning designs of 74 modules over 30 weeks revealed that the workload on a weekly basis on other activities decreased when assessment activities were introduced. This implied that educators at the OU aimed to balance the total workload when designing computer-based assessments (CBA). Secondly, learning designs could explain 69% of weekly behaviour by students in the VLE, which gives teachers a clear mandate to think about the most effective strategies and designs to support students. 

Based upon ten years of learning design research, and a recent surge in large-scale empirical research on learning analytics, in this research programme we identify four research questions that we aim to address in the next two to four years. 

  1. How useful are the learning analytics and learning design data for teachers and students, and how they could be further improved?
  2. What is the optimum balance of learning design and learning analytics to improve retention rates?
  3. What is the students’ voice in learning analytics and learning design?
  4. How can we effectively support teachers and organisations to use learning analytics and learning design?

The Learning Analytics and Learning Design team

Publications

2016