loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Sebastian Kucharski 1 ; Gregor Damnik 2 ; Florian Stahr 1 and Iris Braun 1

Affiliations: 1 Computer Networks Group, Faculty for Computer Science, Technische Universität Dresden, Germany ; 2 Center for Teacher Education and Educational Research, Technische Universität Dresden, Germany

Keyword(s): Automatic Item Generation, AIG, Assessment, Cognitive Model, Item Model.

Abstract: The traditional way of constructing items to assess learning is time-consuming because it requires experts to perform the labor-intensive task of creating legions of items by hand. The Automatic Item Generation (AIG) approach aims to streamline this process by having experts not formulate individual items, but rather create highly structured models that can be used by software to automatically generate them. This requires two types of software components. First, an editor that allows experts to specify these models. Second, a generator that processes the specified models and generates the intended items. The elaboration of these components must address the following challenges. The former must be usable for the definition of complex knowledge models while the corresponding modeling process should be easy to understand. The latter should be able to process these models in a reasonable time. Thus, the goal is to overcome both challenges by defining and conceptualizing the use of a mode l representation that is easy to understand and efficient to process. Therefore, we present a new AIG software toolkit in relation to our previous work, which addresses these challenges by introducing a new representation approach - the layered-model-approach. The toolkit shall be evaluated in terms of usability and efficiency. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.211.24.175

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kucharski, S.; Damnik, G.; Stahr, F. and Braun, I. (2023). Revision of the AIG Software Toolkit: A Contribute to More User Friendliness and Algorithmic Efficiency. In Proceedings of the 15th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-641-5; ISSN 2184-5026, SciTePress, pages 410-417. DOI: 10.5220/0011982000003470

@conference{csedu23,
author={Sebastian Kucharski. and Gregor Damnik. and Florian Stahr. and Iris Braun.},
title={Revision of the AIG Software Toolkit: A Contribute to More User Friendliness and Algorithmic Efficiency},
booktitle={Proceedings of the 15th International Conference on Computer Supported Education - Volume 2: CSEDU},
year={2023},
pages={410-417},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011982000003470},
isbn={978-989-758-641-5},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Computer Supported Education - Volume 2: CSEDU
TI - Revision of the AIG Software Toolkit: A Contribute to More User Friendliness and Algorithmic Efficiency
SN - 978-989-758-641-5
IS - 2184-5026
AU - Kucharski, S.
AU - Damnik, G.
AU - Stahr, F.
AU - Braun, I.
PY - 2023
SP - 410
EP - 417
DO - 10.5220/0011982000003470
PB - SciTePress