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An Education Model for Game Development by A Swedish-Japanese Industry-Academia Alliance

In Proceedings of the 2020 IEEE Conference on Games (CoG), IEEE, Online, 328-335.

Yukiko Sato and Hiroki Hanaoka and Henrik Engström and Shuichi Kurabayashi
August 24, 2020

Abstract

This paper presents and evaluates a new educational tool and a model for learning theory and practice of game development, by involving geographically separated and culturally different industrial and academic organizations. We propose a TRIAD education model, which is as an extension to the conventional project-based learning, by uniting industry staff, faculty members, and students in an online discussion space. This model is a two-phase cyclic model where in Phase-1) industry members and academic staff discuss the progress of PBL transparently, showing the cross-cultural gaps and advising how to solve them, and in Phase-2) students make progress on the projects. Additionally, the TRIAD model adopts asynchronous text messaging as a core communication method, which is suitable for a multi-timezone situation, as well as exchanges of self-reviews from students and feedback from instructors. The online space not only facilitates interns in overcoming the hurdles represented by the gaps between the industry and academia, but it also visualizes the students’ implicit educational progress by periodically analyzing all stored data by applying quantitative text analysis methods. This paper shows the result of an empirical study by utilizing this TRIAD model and analytics tool conducted at a large-scale Japanese game company with over 2,000 employees focusing on two students from the division of game development at a university in Sweden. We applied a quantitative text analysis tool for this model during an on-going internship program to clarify how students change their thoughts and behaviors by acquiring cross-cultural professionalism in the game industry. In addition, the result indicates that this TRIAD model is resilient, even under the COVID-19 epidemic situation due to its text-based approach.

Info

URL: IEEE COG 2020

DOI: 10.1109/CoG47356.2020.9231558

Citation: .bib format

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