Tanja Käser

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Using data from student use of educational technologies to evaluate and improve cognitive models of learners is now a common approach in EDM. Such naturally occurring data poses modeling challenges when non-random factors drive what data is collected. Prior work began to explore the potential parameter estimate biases that may result from data from tutoring(More)
Modeling and predicting student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for student modeling is Bayesian Knowledge Tracing (BKT). BKT models, however, lack the ability to describe the hierarchy and relationships between the different skills of a learning domain. In this work, we therefore aim at increasing the(More)
This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic Bayesian network(More)
The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show low performance for data that inherently suffer from noise, resulting in temporally inconsistent(More)
The adaptivity of intelligent tutoring systems relies on the accuracy of the student model and the design of the instructional policy. Recently an instructional policy has been presented that is compatible with all common student models. In this work we present the next step towards a universal instructional policy. We introduce a new policy that is(More)
This paper introduces a computer-based training program for enhancing numerical cognition aimed at children with developmental dyscalculia. Through modelling cognitive processes and controlling the level of their stimulation, the system optimizes the learning process. Domain knowledge is represented with a dynamic Bayesian network on which the mechanism of(More)
This article presents the design and a first pilot evaluation of the computer-based training program Calcularis for children with developmental dyscalculia (DD) or difficulties in learning mathematics. The program has been designed according to insights on the typical and atypical development of mathematical abilities. The learning process is supported(More)
This 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(More)
In this paper, we explore the possibility of a general framework for modelling engagement dynamics in software tutoring, focusing on the cases of developmental dyslexia and developmental dyscalculia. This project aims at capturing the similar engagement state patterns for the two learning disabilities. We start by presenting a model of engagement dynamics(More)
Computational education offers an important add-on to conventional teaching. To provide optimal learning conditions, accurate representation of students’ current skills and adaptation to newly acquired knowledge are essential. To obtain sufficient representational power we investigate suitability of general graphical models and discuss adaptation by(More)