Carlotta Schatten

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In Intelligent Tutoring Systems, adaptive sequencers can take past student performances into account to select the next task which best fits the student's learning needs. In order to do so, the system has to assess student skills and match them to the required skills and difficulties of available tasks. In this scenario two problems arise: (i) Tagging tasks(More)
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)
—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)
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)