Ruth Janning

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The performance prediction and task sequencing in traditional adaptive intelligent tutoring systems needs information gained from expert and domain knowledge. In a former work a new efficient task sequencer based on a performance prediction system was presented, which only needs former performance information but not the expensive expert and domain(More)
Usually, in intelligent tutoring systems the task sequencing is done by means of expert and domain knowledge. In a former work we presented a new efficient task sequencer without using the expensive expert and domain knowledge. This task sequencer only uses former performances and decides about the next task according to Vygotsky's Zone of Proximal(More)
Recognising students’ emotion, affect or cognition is a relatively young field and still a challenging task in the area of intelligent tutoring systems. There are several ways to use the output of these recognition tasks within the system. The approach most often mentioned in the literature is using it for giving feedback to the students. The features used(More)
GPR is a nondestructive method to scan the subsurface. On the resulting radargrams, originally interpreted manually in a time consuming process, one can see hyperbolas corresponding to buried objects. For accelerating the interpretation a machine shall be enabled to recognize hyperbolas on radargrams autonomously. One possibility is the combination of(More)
Performance prediction has the potential of ameliorate the student model of an Intelligent Tutoring System by predicting whether a student mastered or not a specific set of skills. Recently, it has been shown, by means of a simulated learning process, how performance prediction methods based on Matrix Factorization can be used for continuous score(More)
Ground penetrating radar is a non-destructive method to scan the shallow subsurface for detecting buried objects like pipes, cables, ducts and sewers. Such buried objects cause hyperbola shaped reflections in the radargram images achieved by GPR. Originally, those radargram images were interpreted manually by human experts in an expensive and time consuming(More)
Most of the artificial intelligence and machine learning researches deal with big data today. However, there are still a lot of real world problems for which only small and noisy data sets exist. Hence, in this paper we focus on those small data sets of noisy images. Applying learning models to such data may not lead to the best possible results because of(More)