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New Digital Tools to Teach Soft Skills and Sustainability for the Labor Market: Gamification as a Tool for Competency Acquisition in Realistic Contexts

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Learning Technologies and Systems (ICWL 2022, SETE 2022)

Abstract

This article describes the Compete! educational game, developed within the European Erasmus + framework, which aims to teach soft skills to higher education students to increase their employability. A participatory learning methodology based on a gamification tool has been used for this purpose. The game presents a series of scenarios that describe social sustainability problems and require applying of soft skills identified as key competences in a field study across different European countries. These competences are: creative problem solving, effective communication, stress management, and teamwork. On completion of each game scenario and the game itself, students receive an evaluation of both their soft skills and the strategic and operational decisions they have made. In the evaluation of these decisions, both economic and sustainability aspects of the decision are assessed. The teacher can then address the competences and sustainability issues using the different scenarios of the game, thus creating higher motivation and deeper understanding amongst the students. This hybrid learning methodology incorporates digital tools for the cross-curricular teaching-learning of sustainability and soft skills. It is therefore a methodology that promotes the SDGs.

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Notes

  1. 1.

    https://competeproject.eu/.

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Acknowledgments

This work has been partially co-funded by the European Union, through the Eramus+ Programme (GA 2019-1-IT02-KA203-062350).

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Correspondence to Nadia McGowan .

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Serrano, A.L., McGowan, N., Burgos, D., Moreno-Ger, P. (2023). New Digital Tools to Teach Soft Skills and Sustainability for the Labor Market: Gamification as a Tool for Competency Acquisition in Realistic Contexts. In: González-González, C.S., et al. Learning Technologies and Systems. ICWL SETE 2022 2022. Lecture Notes in Computer Science, vol 13869. Springer, Cham. https://doi.org/10.1007/978-3-031-33023-0_6

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  • DOI: https://doi.org/10.1007/978-3-031-33023-0_6

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  • Online ISBN: 978-3-031-33023-0

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