Abstract:

Gamification, the use of game attributes in non-game context, is used in the private sector as well as education(Deterding, 2012). Research into Gamified Learning (GL) often focuses on case studies of impact or value (Dicheva, Dichev, Agre, & Angelova, 2015), and can suffer from a lack of parsimony(Landers, 2014). The results are frameworks for gamified learning with limited or unclear practical applications and even less research on how to “gamify”. However, consistent among this research are intersections with research in Student Engagement (SE). This presentation describes how a study on gamification’s impact on SE produced an unintentional by-product - a way to create GL without extensive knowledge of gamification, and potentially serving research with a testable technique for “gamifying” in Higher Education (HE).

Existing means for observing GL were insufficient for our study, so we sought to create one via a synthesis of frameworks. First, Bedwell’s taxonomy of “game attributes” (GA’s) isolates and describes the basic parts of any game (Wilson et al., 2009). This helps to identify the presence of GA’s in learning. Next, the use of those GA’s must conform to a definition of GL that’s rigorous enough to be measured. Landers “Theory of Gamified Learning” (ToGL), contextualises GL as an intervention that identifies, extracts, and embeds GA’s into learning (Landers, 2014) where GA’s, instructional content, and students’ “behaviours and attitudes” (B/A’s) work together to affect learning. Encouragingly, much of the individual B/A’s described in the ToGL are SE concepts. If SE can stand in for B/A’s in the ToGL, then SE research may hold the key gamification.

For this, we turned to Ella Kahu’s consolidation of multiple SE frameworks in HE (Kahu, 2013). This breaks SE down into measurable variable-states ideal for Landers’ B/A’s, completing our framework for observing GL’s impact on SE. However, in the process, Kahu’s work also implies that relationships between GA’s and SE variable-states are explainable by many learning theories. In which case, almost any learning theory could be used to select GA’s to target SE variable-states, using our newly completed observational framework. Rather than observing GL, you could create it.

The result is a step-by-step methodology for gamifying formative assessments. Here, Kahu’s SE framework is mapped onto a player’s engagement (PE) with a game, and a learning theory of their choice explains how it’s GA’s affect (PE), and thus SE. This allows for the identification, extraction, and embedding of game attributes, targeting SE for learning. The methodology is already in test phases with staff members at Edinburgh Napier University.

References:
[1] Deterding, S. (2012). Gamification: Designing for Motivation. Interactions, 19(4), 14.
[2] Dicheva, D., Dichev, C., Agre, G., & Angelova, G. (2015). Gamification in Education: A Systematic Mapping Study. Educational Technology & Society, 18(3), 75–88.
[3] Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758–773.
[4] Landers, R. N. (2014). Developing a Theory of Gamified Learning: Linking Serious Games and Gamification of Learning. Simulation & Gaming, 45(6), 752–768.
[5] Wilson, K. A., Bedwell, W. L., Lazzara, E. H., Salas, E., Burke, C. S., Estock, J. L., … Conkey, C. (2009). Relationships Between Game Attributes and Learning Outcomes Review and Research Proposals. Simulation & Gaming, 40(2), 217–266.

Keywords:

Gamificaition, theory, assessment, learning and teaching, methodology.