Visualizing Repertory Grid Data for Formative Assessment Kostas Pantazos 1 , Ravi Vatrapu 1, 2 and Abid Hussain 1 1 Computational Social Science Laboratory (CSSL) Department of IT Management, Copenhagen Business School 2 Norwegian School of Information Technology (NITH) {kp.itm, vatrapu, ah.itm}@cbs.dk Abstract. Repertory grid tools systematically collect data that consists of a top- ic, its elements, constructs and element ratings. Gaining meaningful insights from repertory grids data is a challenge because data analysis is time- consuming and a significant mental effort is needed. Visualizations aim at facil- itating data analysis through a visual and interactive approach, which allows us- ers to understand their data, reflect, and make better decisions. This paper pre- sents an interactive visualization tool for teachers and students. The tool visual- izes repertory grid data using two dashboards, where teachers and students can investigate constructs and rating elements of students at the individual or group level. Visualizing the repertory grid data is an initial attempt towards teaching analytics. Future work will focus on evaluating the tool in a real setting with teachers and students, and collecting suggestions for improvement. 1 Introduction The advancement of technology has enhanced data generation for personal and professional use – know as Big Data. Instead of producing WORN data (write-once, read-never coined by Powsner and Tufte), researchers are continuously studying data from different fields in order to benefit and to extract knowledge. This also is the goal of Learning Analytics (LA). LA is “the collection and analysis of usage data associat- ed with student learning” and aims at improving education through interventions after observing and understanding learning behaviors (Brown, 2011). Nowadays, data collection is less challenging than data analysis, because the latter requires additional cognitive effort. Data analysis can be enhanced using visual repre- sentation. Information visualization enhances human cognition by visually presenting abstract data and revealing patterns, trends and outliers (Card et al., 1999). Visual Analytics is the science of combining interactive visualizations with analytical rea- soning techniques to enable users to understand their data, reflect more effectively, and make better decisions (Keim et al., 2010).