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Auflistung nach Autor:in "Schrumpf, Johannes"

1 - 3 von 3
  • Konferenzbeitrag
    Development of a Digital Goal Setting Companion for Higher Education
    (DELFI 2021, 2021) Weber, Felix; Schrumpf, Johannes; Thelen, Tobias
    Goal setting is known to be an effective way to guide behaviour and plays an essential role in self-regulated learning. Goals can serve as benchmarks for the evaluation of behaviour. Recently, research on goal networks instead of isolated goals has received growing interest. In this paper, we present a goal setting intervention that guides university students a) to develop personal educational goals and b) to derive sub-goals, actions and strategies to make those high-level goals tractable. The results are hierarchical goal systems connecting high-level goals to concrete actions. We illustrate the technical implementation as web-based application. Explorative data analysis of data from a paper-pencil preliminary study (n=8) and a first pilot study with a web-based software prototype (n=17) is presented. We conclude with an outlook on further development steps.
  • Konferenzbeitrag
    A Neural Natural Language Processing System for Educational Resource Knowledge Domain Classification
    (DELFI 2021, 2021) Schrumpf, Johannes; Weber, Felix; Thelen, Tobias
    In higher education, educational resources are the vessel with which information get transferred to the learner. Information on the content discussed in the scope of the educational resources, however, is implicit and must be inferred by the user by reading the resource title or through contextual information. In this paper we present a state-of-the-art neural natural language processing system, based on Google-BERT, that maps educational resource titles into one of 905 classes from the Dewey Decimal Classification (DDC) system. We present model architecture, training procedure dataset properties and our performance analysis methodology. We show that aside from classification performance, our model implicitly learns the class hierarchy inherent to the DDC.
  • Konferenzbeitrag
    Re-thinking Transformer based educational resource recommendation engines for higher education
    (20. Fachtagung Bildungstechnologien (DELFI), 2022) Schrumpf, Johannes; Thelen, Tobias
    Digital Study Assistant (DSA) systems for higher education seek to support learners in identifying, structuring and pursuing their personal educational goals. One strategy to achieve this is to galvanize learner interest in engaging with educational resource beyond the scope of their known, pre-determined curriculum. For this purpose, DSA systems may provide a recommendation engine that matches learner interests in natural language to an educational resource covering the topic of interest. To offer a rich assortment of educational resources, these resources need to be fetched from multiple sources such as MOOC and OER repositories or from the learning management system of a local University. In a previous publication, we have presented SidBERT, a BERT-based natural language processing neural network for educational resource classification and recommendation which has been in active use in a prototypical Digital Study Assistant system. This work seeks to follow up on the SidBERT architecture, by introducing an evolution of SidBERT, SemBERT, that is capable of comparing educational resources on a more fine-grained level, thereby addressing multiple shortcomings of the SidBERT architecture and its application within the DSA software. We present network architecture, training parameters and evaluate SemBERT on two datasets. We compare SemBERT to SidBERT and discuss the implications of SemBERT for DSA systems at large.