Massively Scalable Learning: Principles of Serious Game Scalability
Dominicus Tornqvist, Lian Wen, Griffith University, Australia ; Jennifer Tichon, Queensland University of Technology, Australia ; Guangdong Bai, Griffith University, Australia
Journal of Interactive Learning Research Volume 32, Number 2, ISSN 1093-023X Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC
There is a healthy research community focused on individual differences to tailor serious games for maximum effect for each person. But there is a comparative lack of research on the scalability of serious games for maximising knowledge saturation in a population. Scalability is critical in many real applications. The authors detail this neglected set of priorities as a research paradigm: Massively Scalable Learning (MSL), and delineate what kinds of domains would benefit most from MSL, summarise its specific cognitive, motivational, and practical components, and detail the factors and mechanisms that determine if MSL is relevant and effective. Existing research is examined, evaluating common educational tools and game features such as virtual tutors for their applicability to MSL, to extract some initial guidelines and principles for MSL for practitioners of serious game design, and identify the key knowledge gaps where future research needs to focus in this neglected but important area.
Tornqvist, D., Wen, L., Tichon, J. & Bai, G. (2021). Massively Scalable Learning: Principles of Serious Game Scalability. Journal of Interactive Learning Research, 32(2), 99-124. Waynesville, NC: Association for the Advancement of Computing in Education (AACE).
© 2021 Association for the Advancement of Computing in Education (AACE)