Severin Klingler

Beyond Knowledge Tracing: Modeling Skill Topologies with Bayesian Networks

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 representational power of the student model by employing dynamic Bayesian networks that are able to represent such skill topologies. To ensure model interpretability, we constrain the parameter space. We evaluate the performance of our models on five large-scale data sets of different learning domains such as mathematics, spelling learning and physics, and demonstrate that our approach outperforms BKT in prediction accuracy on unseen data across all learning domains.

When to stop?: towards universal instructional policies

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 applicable to an even wider range of student models including DBNs modeling skill topologies and forgetting. We theoretically and empirically compare our policy to previous policies. Using synthetic and real world data sets we show that our policy can effectively handle wheel-spinning students as well as forgetting across a wide range of student models.

Towards a Framework for Modelling Engagement Dynamics in Multiple Learning Domains

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 in spelling learning, which relates input behaviour to learning and explains the dynamics of engagement states. Predictive power of extracted features is increased by incorporating domain knowledge in the pre-processing. The introduced model enables the prediction of focused and receptive states, and of forgetting. In the second part, we extend the model to a more general framework, which takes into account the similarities and dissimilarities of the two studied cases. Finally, we define desirable properties of a general engagement dynamics model, while analysing the reusability of the introduced spelling model.

Modeling engagement dynamics in spelling learning

In this paper, we introduce a model of engagement dynamics in spelling learning. The model relates input behavior to learning, and explains the dynamics of engagement states. By systematically incorporating domain knowledge in the preprocessing of the extracted input behavior, the predictive power of the features is significantly increased. The model structure is the dynamic Bayesian network inferred from student input data: an extensive dataset with more than 150 000 complete inputs recorded through a training software for spelling. By quantitatively relating input behavior and learning, our model enables a prediction of focused and receptive states, as well as of forgetting.

Dynamic Bayesian Networks for Student Modeling

Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling. The structure of BKT models, however, makes it impossible to represent the hierarchy and relationships between the different skills of a learning domain. Dynamic Bayesian networks (DBN) on the other hand are able to represent multiple skills jointly within one model. In this work, we suggest the use of DBNs for student modeling. We introduce a constrained optimization algorithm for parameter learning of such models. We extensively evaluate and interpret the prediction accuracy of our approach on five large-scale data sets of different learning domains such as mathematics, spelling learning, and physics. We furthermore provide comparisons to previous student modeling approaches and analyze the influence of the different student modeling techniques on instructional policies. We demonstrate that our approach outperforms previous techniques in prediction accuracy on unseen data across all learning domains and yields meaningful instructional policies.

Stealth Assessment in ITS - A Study forźDevelopmental Dyscalculia

Intelligent tutoring systems are adapting the curriculum to the needs of the student. The integration of stealth assessments of student traits into tutoring systems, i.e. the automatic detection of student characteristics has the potential to refine this adaptation. We present a pipeline for integrating automatic assessment seamlessly into a tutoring system and apply the method to the case of developmental dyscalculia DD. The proposed classifier is based on user inputs only, allowing non-intrusive and unsupervised, universal screening of children. We demonstrate that interaction logs provide enough information to identify children at risk of DD with high accuracy and validity and reliability comparable to traditional assessments. Our model is able to adapt the duration of the screening test to the individual child and can classify a child at risk of DD with an accuracy of 91i?ź% after 11i?źmin on average.

HeapCraft: interactive data exploration and visualization tools for understanding and influencing player behavior in Minecraft

We present HeapCraft: an open-source suite of interactive data exploration and visualization tools that allows researchers, server administrators and game designers to analyze and potentially influence player behavior in Minecraft. Our framework includes a telemetry system, several tools for visualizing and representing the collected data, and tools for modifying the game experience in controlled ways. Measures that we use to quantify and visualize player behavior and collaboration have been derived from a large data set containing 3451 player-hours from 908 players and 43 different servers. HeapCraft has been demonstrated on a variety of tasks including player behavior classification, as well as quantifying and improving collaboration of players on Minecraft servers. HeapCraft is freely available and serves to democratize game analytics for the Minecraft community at large.