Generating LADs that Make Sense

Abstract

Learning Analytics Dashboards (LADs) deliver rich and actionable representations of learning data to support meaningful and insightful decisions that ultimately leverage the learning process. Yet, because of their limited adoption and the complex nature of learning data, their design is still a major area of inquiry. In this paper, we propose to expand LAD codesign approaches. We first investigate how the user makes sense of the data delivered by LADs and how to support this sensemaking process at design. Second, we propose a generative tool, supporting sensemaking and decision making process, that extends end-users participation during the prototyping phase and empowers LAD designers. We also present an evaluation of the tool, including usability and user experience, demonstrating its effectiveness in supporting the design and prototyping of LADs.