Abstract
Simulation-based learning is a method in which learners learn to master real-life scenarios and tasks from simulated application contexts. It is particularly suitable for the use of VR technologies, as these allow immersive experiences of the targeted scenarios. VR methods are also relevant for studies on online learning, especially in groups, as they provide access to a variety of multimodal learning and interaction data. However, VR leads to a trade-off between technological conditions of the observability of such data and the openness of learner behavior. We present Va.Si.Li-Lab, a VR-L ab for Simulation-based Learn ing developed to address this trade-off. Va.Si.Li-Lab uses a graph-theoretical model based on hypergraphs to represent the data diversity of multimodal learning and interaction. We develop this data model in relation to mono- and multimodal, intra- and interpersonal data and interleave it with ISO-Space to describe distributed multiple documents from the perspective of their interactive generation. The paper adds three use cases to motivate the broad applicability of Va.Si.Li-Lab and its data model.
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Notes
- 1.
For an earlier approach to this concept, see [52].
- 2.
VAnnotatoR ’s browser enables immersive visualization of multimedia information units. These include texts, images, and videos that can be mapped to linkable discourse referents as meaning representations of entities manifested in these information units that can themselves be manipulated as 3D objects.
- 3.
- 4.
We refer to the notion of directed hypergraphs of [28].
- 5.
- 6.
This can lead to redundancy in our model, since some of these attributes are explicitly modeled using hypergraph structures (see below), but this saves us from enumerating all the attributes that do not create redundancy.
- 7.
- 8.
Here we leave ISO-Space by allowing multiple triggers for the same link. This is necessary, for example, to account for alignment between interlocutors.
- 9.
TIMEX3 values according to ISO-TimeML [69] are an alternative here, but since we consider measurement operations using Va.Si.Li-Lab, we restrict ourselves to timestamps in this definition. We also deviate from ISO-Space 2.0 in that we refer to (s-)motions as entities that allow us to relate, e.g., the type or manner of motion and its temporal structure to (s-)event paths: the same path can then be related to different motion events. Since our task here is not to annotate spatial relations in utterances or texts, but to capture spatial relations using motion and behavioral data in Va.Si.Li-Lab, we take this route of adapting ISO-Space.
- 10.
Of course, \(\sigma _v\) goes beyond the realm of ISO-Space.
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Acknowledgement
We especially thank all participants involved in the experiments with the help of Va.Si.Li-Lab for their support. This work was co-funded by Bundesministerium für Bildung und Forschung (BMBF), grant 01JD1906B, as well as the “Digital Teaching and Learning Lab” (DigiTeLL) at the Goethe University Frankfurt. Furthermore, this work was supported by the Deutsche Forschungsgemeinschaft (DFG) [grant numbers ME 2746/10-1 and LU 2114/2-1].
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Mehler, A. et al. (2023). A Multimodal Data Model for Simulation-Based Learning with Va.Si.Li-Lab. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14028. Springer, Cham. https://doi.org/10.1007/978-3-031-35741-1_39
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