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Auflistung nach Autor:in "Gross, Sebastian"

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  • Konferenzbeitrag
    Feedback provision strategies in intelligent tutoring systems based on clustered solution spaces
    (DeLFI 2012: Die 10. e-Learning Fachtagung Informatik der Gesellschaft für Informatik e.V., 2012) Gross, Sebastian; Mokbel, Bassam; Hammer, Barbara; Pinkwart, Niels
    Designing an Intelligent Tutoring System (ITS) usually requires precise models of the underlying domain, as well as of how a human tutor would respond to student mistakes. As such, the applicability of ITSs is typically restricted to welldefined domains where such a formalization is possible. The extension of ITSs to ill-defined domains constitutes a challenge. In this paper, we propose the provision of feedback based on solution spaces which are automatically clustered by machine learning techniques operating on sets of student solutions. We validated our approach in an expert evaluation with a data set from a programming course. The evaluation confirmed the feasibility of the proposed feedback provision strategies.
  • Zeitschriftenartikel
    Learning Feedback in Intelligent Tutoring Systems
    (KI - Künstliche Intelligenz: Vol. 29, No. 4, 2015) Gross, Sebastian; Mokbel, Bassam; Hammer, Barbara; Pinkwart, Niels
    Intelligent Tutoring Systems (ITSs) are adaptive learning systems that aim to support learners by providing one-on-one individualized instruction. Typically, instructing learners in ITSs is build on formalized domain knowledge and, thus, the applicability is restricted to well-defined domains where knowledge about the domain being taught can be explicitly modeled. For ill-defined domains, human tutors still by far outperform the performance of ITSs, or the latter are not applicable at all. As part of the DFG priority programme “Autonomous Learning”, the FIT project has been conducted over a period of 3 years pursuing the goal to develop novel ITS methods, that are also applicable for ill-defined problems, based on implicit domain knowledge extracted from educational data sets. Here, machine learning techniques have been used to autonomously infer structures from given learning data (e.g., student solutions) and, based on these structures, to develop strategies for instructing learners.
  • Konferenzbeitrag
    Orientation and Navigation Support in Resource Spaces Using Hierarchical Visualizations
    (i-com: Vol. 16, No. 1, 2017) Gross, Sebastian; Kliemannel, Marcel; Pinkwart, Niels
    In this article we investigate how orientation and navigation in (extensive) spaces consisting of digital resources can be supported by using hierarchical visualizations. Such spaces can consist of heterogeneous sets of digital resources as for instance articles from Wikipedia, textbooks, and videos. Due to easier access to digital resources in the Internet age, a manual exploration of these spaces might lead to information overload. As a result, techniques need to be developed in order to automatically analyze and structure sets of resources. We introduce a prototypical implementation of a visualization pipeline that extracts information dimensions from resources in order to group them into semantically similar clusters, and visualizes these clusters using two different visualizations: a treemap visualizing clusters and nested subclusters, and a rooted tree visualizing groups of semantically similar resources as subtrees. In a lab study we evaluated the two visualizations and compared them to two control groups. The results may hint to users’ better understanding of the resources’ underlying knowledge as compared to using typical approaches (e.g. web search results as list) when using hierarchical visualizations.