This paper is dedicated to finding anomalies in short multivariate time series and focus on analysis of educational data. We present ODEXEDAIME, a new method for automated finding and visualising anomalies that can be applied to different types of short multivariate time series. The method was implemented as an extension of EDAIME, a tool for visual data mining in temporal data that has been successfully used for various academic analytics tasks, namely its Motion Charts module. We demonstrate a use of ODEXEDAIME on analysis of computer science study fields.
CITATION STYLE
Géryk, J., Popelínský, L., & Triščík, J. (2016). Visual Anomaly Detection in Educational Data (pp. 99–108). https://doi.org/10.1007/978-3-319-44748-3_10
Mendeley helps you to discover research relevant for your work.