The Path between Pedagogy and Technology: Establishing a Theoretical Basis for the Development of Educational Game Environments

The power of computer game technology is currently being harnessed to produce “serious games”. These “games” are targeted at the education and training marketplace, and employ various key game-engine components such as the graphics and physics engines to produce realistic “digital-world” simulations of the real “physical world”. Many approaches are driven by the technology and often lack a consideration of a firm pedagogical underpinning. The authors believe that an analysis and deployment of both the technological and pedagogical dimensions should occur together, with the pedagogical dimension providing the lead. This chapter explores the relationship between these two dimensions, and explores how “pedagogy may inform the use of technology”, how various learning theories may be mapped onto the use of the affordances of computer game engines. Autonomous and collaborative learning approaches are discussed. The design of a serious game is broken down into spatial and temporal elements. The spatial dimension is related to the theories of knowledge structures, especially “concept maps”. The temporal dimension is related to “experiential learning”, especially the approach of Kolb. The multi-player aspect of serious games is related to theories of “collaborative learning” which is broken down into a discussion of “discourse” versus “dialogue”. Several general guiding principles are explored, such as the use of “metaphor” (including metaphors of space, embodiment, systems thinking, the internet and emergence). The topological design of a serious game is also highlighted. The discussion of pedagogy is related to various serious games we have recently produced and researched, and is presented in the hope of informing the “serious game community”.

Unreal PowerPoint™: Immersing PowerPoint Presentations in a Virtual Computer Game Engine World

At a recent conference on games in education, we made a radical decision to transform our standard presentation of PowerPoint slides and computer game demonstrations into a unified whole, inserting the PowerPoint presentation to the computer game. This opened up various questions relating to learning and teaching theories, which were debated by the conference delegates. In this paper, we reflect on these discussions, we present our initial experiment, and relate this to various theories of learning and teaching. In particular, we consider the applicability of “concept maps” to inform the construction of educational materials, especially their topological, geometrical and pedagogical significance. We supplement this “spatial” dimension with a theory of the dynamic, temporal dimension, grounded in a context of learning processes, such as Kolb’s learning cycle. Finally, we address the multi-player aspects of computer games, and relate this to the theories of social and collaborative learning. This paper attempts to explore various theoretical bases, and so support the development of a new learning and teaching virtual reality approach.

Mapping of Submerged Aquatic Vegetation in Rivers From Very High Resolution Image Data, Using Object Based Image Analysis Combined with Expert Knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy.