Facilitating the Use of Data From Multiple Sources for Formative Learning in the Context of Digital Assessments: Informing the Design and Development of Learning Analytic Dashboards - Kannan, Priya; Zapata-Rivera, Diego (Frontiers in Education, 2022)
Learning analytic dashboards (LADs) are data visualization systems that use dynamic data in digital learning environments to provide students, teachers, and administrators with a wealth of information about student’s engagement, experiences, and performance on tasks. LADs have become increasingly popular, particularly in formative learning contexts, and help teachers make data-informed decisions about a student’s developing skills on a topic. LADs afford the possibility for teachers to obtain real-time data on student performance, response processes, and progress on academic learning tasks. However, data presented on LADs are often not based on an evaluation of stakeholder needs, and have been found to not be clearly interpretable and actionable (Kuosa et al., 2016; Molenaar & Knoop-van Campen, 2018) for teachers to readily adapt their pedagogical actions based on these insights. We elaborate on how insights from research focused on interpretation and use of Score Reporting systems (Hambleton & Zenisky, 2013; Zapata-Rivera, 2019; 2020) and research on open learner models (OLMs) (Bodily et al., 2018; Bull, 2020; Sergis, & Sampson, 2019; Zapata-Rivera, 2020) can be used to inform a research agenda aimed at exploring the design and evaluation of LADs.