Learning Design (LD) and Learning Analytics (LA) are both domains of research and action that aim to improve learning effectiveness. In Learning Design or, as some prefer, Design for Learning, practitioners are interested in understanding how the processes undertaken by teachers and trainers can be made visible, shared, exposed to scrutiny, and consequently made more effective and efficient. On the other hand, Learning Analytics are defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs”. LA typically employ large datasets to provide real-time or retrospective insights about the effect and effectiveness of various elements and features of learning environments.
According to situational approaches, one of the prerequisites to obtain relevant outputs is not to isolate the analysis of educational data from the context in which it is embedded. Following this perspective, some authors state that this tandem between LD and LA offers the opportunity to better understand student behaviour and provide pedagogical recommendations when deviations from the original pedagogical intention emerge, addressing one of the challenges posed by LA, i.e.: interpreting the resulting data against the original pedagogical intent and the local context, to evaluate the success of a particular learning activity.
This approach of linking LD and LA has been already applied to support learning in different contexts (such as blended, on-line or computer-supported collaborative learning), scales (from small classrooms to MOOCs), and abstraction levels (from particular learning activities to the accomplishment of the curriculum objectives defined in a course). Reciprocally, LA can be helpful to inform teachers on the success and outcomes of their learning designs, e.g., providing evidence of the design impact on aspects such as engagement, learning paths, time consumed to complete the activities, etc. These data can support awareness and reflection about the effects of the learning designs as well as redesign processes, by facilitating the identification of design elements that need to be revised before reuse.
To sum up, LD offers LA a domain vocabulary, representing the elements of a learning system to which analytics can be applied. LA in turn, offers LD a higher degree of rigor by validating or refuting assumptions about the effects of various designs in diverse contexts. Thus, there is a synergistic relationship between both domains, which has led to a growing interest and some initial effort in bringing them together. However, making these links operational and coherent is still an open challenge.