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Assessment Framework for Immersive Learning: Application and Evaluation

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Immersive Learning Research Network (iLRN 2023)

Abstract

Technological advances, changing demands of students and employers as well as the stiff competition among higher education (HE) institutions are leading to the exploration and adoption of innovative learning technologies in HE. The use of immersive technologies for education is rising given their potential to complement current methods of delivering learning content to students. Among the benefits of these technologies to education is their ability to promote active, experiential and higher-order learning. However, what is still unclear to educators is how to measure learning in immersive environments. This paper proposes a framework developed to guide educators through the process of designing and implementing assessments when using immersive technologies. The assessment framework presented in this paper builds upon the principle of Constructive Alignment (CA) and the Evidence-Centered Design (ECD) framework. A qualitative evaluation of the proposed framework was conducted to evaluate how easy it is for educators to understand and use it for assessment design around immersive learning activities. The framework was found to provide familiar and user-friendly assessment design guidelines for educators. Lastly, this paper also presents a suggestion for the application of the proposed assessment framework to the design of assessments for an augmented reality game. It also outlines best practices when considering immersive technologies for assessments in HE.

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Acknowledgement

This project has received funding from the European Union’s EU Framework Programme for Research and Innovation Horizon 2020 under Grant Agreement No [812716].

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Correspondence to Jarka Glassey .

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Udeozor, C., Dominguez Alfaro, J.L., Glassey, J. (2024). Assessment Framework for Immersive Learning: Application and Evaluation. In: Bourguet, ML., Krüger, J.M., Pedrosa, D., Dengel, A., Peña-Rios, A., Richter, J. (eds) Immersive Learning Research Network. iLRN 2023. Communications in Computer and Information Science, vol 1904. Springer, Cham. https://doi.org/10.1007/978-3-031-47328-9_15

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  • DOI: https://doi.org/10.1007/978-3-031-47328-9_15

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