Käser, TanjaBusetto, Alberto G.Solenthaler, BarbaraKohn, JulianeAster, Michael vonGross, Markus2020-07-142020-07-142020-07-14201310.1007/978-3-642-39112-5_40https://infoscience.epfl.ch/handle/20.500.14299/170071This paper introduces a method to predict and analyse students' mathematical performance by detecting distinguishable subgroups of children who share similar learning patterns. We employ pairwise clustering to analyse a comprehensive dataset of user interactions obtained from a computer-based training system. The available data consist of multiple learning trajectories measured from children with developmental dyscalculia, as well as from control children. Our online classification algorithm allows accurate assignment of children to clusters early in the training, enabling prediction of learning characteristics. The included results demonstrate the high predictive power of assignments of children to subgroups, and the significant improvement in prediction accuracy for short- and long-term performance, knowledge gaps, overall training achievements, and scores of further external assessments.LearningPredictionDyscalculiaFeature processingPairwise clusteringtext::conference output::conference proceedings::conference paper