Rediscovering the Uptake of Dashboard Feedback: A Conceptual Replication of Foung (2019)

Abstract

Learning analytics has been widely used in the context of language education. Among the studies that have used this approach, many have developed a dashboard that aims to provide students with recommendations based on data so that they can act on these suggestions and improve their performance. To further our understanding of dashboard research, this study aims to replicate an earlier study using a new data mining strategy, association rule mining, to explore if the new strategy can (1) generate comparable results; and (2) provide new insights into feedback uptake in dashboard systems. The original study was conducted with 423 students at a Hong Kong university and implemented a dashboard for a suite of first-year composition courses. It used a classification tree to identify factors that could predict the uptake of tool-based and general recommendations made by the dashboard. After performing association rule mining with the original data set, this study found that this approach allowed for the identification of additional useful factors associated with the uptake of general and tool-based recommendations with a higher accuracy rate. The results of this study provide new insights for dashboard research and showcase the potential use of association rule mining in the context of language education.