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Instructional Techniques to Facilitate Learning and Motivation of Serious GamesReflections on Serious Games

Instructional Techniques to Facilitate Learning and Motivation of Serious Games: Reflections on... [This chapter comments on the contributions in this edited volume and identifies some challenges for future research on serious games. The contributors used rigorous experimental methods to systematically assess the impact of many components of serious games on learning and motivation. The games are serious because there is alignment with relevant instructional content in educational curricula and there is an assessment of associated knowledge, skills, and strategies. The chapters report learning gains for the games compared to comparison conditions, as well as the added value of several game features, such as multimedia, realism, challenge, adaptivity, feedback, interactivity, modeling, collaboration, competition, reflection, fantasy, narrative, and so on. These features are highly correlated in most games so it is difficult to assign credit to particular features when they are implemented in conjunction with many other features. Additional challenges emerge when the games target deep learning of difficult material: (1) game features imposing extraneous cognitive load on working memory, (2) incompatibilities in the timing of feedback to optimize deep learning versus motivation, and (3) control struggles between the game agenda and students’ self-regulated learning. It is argued that researchers could be more involved in the building of games under the guidance of scientific principles even though there are difficulties in the design process and in attempts to scale up researcher-designed serious games. The chapter ends with a quandary in assessing psychological constructs in serious games that are adaptive to the learner.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Instructional Techniques to Facilitate Learning and Motivation of Serious GamesReflections on Serious Games

Part of the Advances in Game-Based Learning Book Series
Editors: Wouters, Pieter; van Oostendorp, Herre

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References (29)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2017
ISBN
978-3-319-39296-7
Pages
199 –212
DOI
10.1007/978-3-319-39298-1_11
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter comments on the contributions in this edited volume and identifies some challenges for future research on serious games. The contributors used rigorous experimental methods to systematically assess the impact of many components of serious games on learning and motivation. The games are serious because there is alignment with relevant instructional content in educational curricula and there is an assessment of associated knowledge, skills, and strategies. The chapters report learning gains for the games compared to comparison conditions, as well as the added value of several game features, such as multimedia, realism, challenge, adaptivity, feedback, interactivity, modeling, collaboration, competition, reflection, fantasy, narrative, and so on. These features are highly correlated in most games so it is difficult to assign credit to particular features when they are implemented in conjunction with many other features. Additional challenges emerge when the games target deep learning of difficult material: (1) game features imposing extraneous cognitive load on working memory, (2) incompatibilities in the timing of feedback to optimize deep learning versus motivation, and (3) control struggles between the game agenda and students’ self-regulated learning. It is argued that researchers could be more involved in the building of games under the guidance of scientific principles even though there are difficulties in the design process and in attempts to scale up researcher-designed serious games. The chapter ends with a quandary in assessing psychological constructs in serious games that are adaptive to the learner.]

Published: Nov 1, 2016

Keywords: Deep learning; Game design

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