Whether it is Fortnite or the emergence of the professional gaming industry, video games and the psychology that underlies their effectiveness have created a revolution when it comes to engaging people both young and old. Despite the enhancement of graphics or plot line within a virtual world, the draw to games has rarely embedded a learning component within this gamified world.
With that said, there has been an increase in the use of games for learning in the secondary and post-secondary sectors and even a small uptick in training and development in the workforce space. Why? Well it makes sense for years educators have attempted to meet students “where they are at” whether through pre-assessment methods, use of contextual lessons, or the incorporation of technology into curricular and co-curricular activities. Even within the industry, training specialists are addressing how game-based learning could contribute to learning motivation and perhaps better job performance.
The question all of this raises is: do these game-based learning scenarios even make a difference or are we just playing games? Then if these games do make a difference in learning comprehension, job performance, and motivation – where do we go from here?
In this post, I attempt to investigate what is the effectiveness of game-based learning as applied to education and workforce training and as a result how to continue to address learning with game-based psychology.
So, get ready, grab a seat, log in, and let’s play!
Although there appears to be a surge in game-based training both for curricular and co-curricular use, research addressing gamification as a learning tool dates back over 25 years.
In fact, according to Randall et al (1992), it was in mathematics that was the first course content area to show “promise” for learning through game-based design. Following the focus on mathematics, other studies illustrated the usage in game-based learning for student support structures such as tutoring, exploring new concepts, and even to “promote self-esteem (Dempsey (1996)). In addition, in the area of training, Wolfe (1997) found that incorporating business games into company training lead to significant increased levels of knowledge especially in the area of strategic management.
On the flip-side, there have also been mixed reviews as to the preferred delivery method with Hays (2005) reviewing 48 empirical studies and showed no evidence that games are the preferred instructional method in all situations.
Yet despite the different research conclusions, there continues to be an increase in incorporating game-based design into both academic and workforce training content.
Where do we go from here?
Given the underpinnings of learning opportunities by applying-game based psychology into training concepts, there is significant potential to utilize these principles while ensuring that learning is measured and reinforced.
One way to ensure learning is to make sure all components of the learning design are measured and aligned. In fact, Ke (2009) found that the most effective usage of game-based design and instructional implementation focused on aligning “learning, learner, and instructional game design.” (pg). Therefore, building upon a framework that aligns the various learning theories, learning outcomes, and game-based design makes sense.
As an example, in a framework of learning outlined by Wu et al. (2012), game-based activities and assessments can align learning theory coupled with personalization and established learning outcomes. In Wu et al (2012)’s framework, the key learning concepts include: Behaviorism, Cognitivism, Humanism, and Constructivism. Each learning theory can be defined as the following (Wu et. al, 2012):
- Behaviorism: learning is produced by stimulation and reinforcement.
- Cognitivism: learning is more than simple stimulation and reinforcement, but to involve thinking.
- Humanism: learning should be student-centered and personalized, and the educator should act as a facilitator.
- Constructivism: learning is an active, constructive process.
Now let’s not get to academic here but these learning theories provide background to think about in developing content with various learning styles and game-based activities that can address some or even all of these various concepts.
Building upon this established framework, it is possible to develop a common learning structure where both the learner and game learn together. For instance, the development of characters in gaming is common and the gaming platform tracks your preferences and performance to better align your experience. Therefore, the game learns more about you as you play more. In a training and learning world of adaptive learning and customized content, the landscape of learning and training is prime to utilize the opportunity for the platform to learn about you while you learn. What better way to support a learn than through a smart sage, a guide who understands how you learn and what content you would prefer and then goes and finds it for you.
So, what could this look like?
Well, we have witnessed adaptive gaming in which those with special challenges are able to communicate with the gaming system through responsive adaption in gaming controllers. To build upon that, we also can realize that educational games can provide an adaptive architecture, and we can look at the work of Pierce, Conlan, and Wade (2010) who found that adaptive educational games could truly illustrate educational benefits and ensure both intrinsic motivation and content flow.
Now what? Let’s couple learning theory, outcomes to achieve, game-based activity, summative and formative assessments, and key personalization and adaptive qualities. The intention in combining these key concepts in the pre-development phase is that curriculum designers, instructors, and trainers will be able to ensure that learning is occurring through several effectiveness measures. We can take those learning theories and reshape them to fit the instructional model and there have a better idea of how effective the game-based content addresses the various learning components while also ensuring the alignment of Ke’s “learning, learner, and game-based instructional methodology.”
So here is a potential to framework to base development upon:
|Behaviorism (Stimulation & Reinforcement Concepts)|
|Cognitivism (Critical Reasoning)|
|Humanism (Personalized Context)|
|Constructivism (active learning process)|
By utilizing this framework, content developers can work from a similar structure to align learning, activities, assessments and adaptive learning structures. While this may take more work on the front end, this reinforcement model can ensure not only that learning outcomes are being addressed and assessed but as content is revised designers can understand the foundational activities and assessments. Working from a framework allows for an architecture for mutual learning for both the learner and the game.
So, are we playing games? Well yes, we are, and they are playing us too.
Dempsey, J. V., Rasmussen, K., & Lucassen, B. (1996). Instructional gaming: implications for instructional technology. In Proceedings of the Annual Meeting of the Association for Educational Communications and Technology, Nashville, TN.
Hays, R. T. (2005). The effectiveness of instructional games: A literature review and discussion. <http://www.dtic.mil/cgi-bin/GetTRDoc?Location.U2&doc.GetTRDoc. pdf&AD.ADA441935> Accessed 25.12.10.
Ke, F. (2009). A qualitative meta-analysis of computer games as learning tools. In R. E. Ferdif (Ed.), Handbook of research on effective electronic gaming in education (pp. 1–32) New York: Hershey.
Peirce, N., Owen C., & Wade, V (2010). Adaptive Educational Games: Providing Non-invasive Personalised Learning Experiences. Second IEEE International Conference on Digital Games and Intelligent Toys Based Education.
Randel, J. M., Morris, B., Wetzel, C., & Whitehill, B. (1992). The effectiveness of games for educational purposes: a review of recent research. Simulation & Gaming, 23(3), 261.
Wu et al (2012). Re-exploring game-assisted learning research: The perspective of learning theoretical bases. Computers & Education 59, 1153–1161.