Pervasive and augmented games for learning address a broad variety of topics, target groups and educational domains. The games we included comprised domains such as history learning (Blum et al., 2012), geometry (Wijers et al., 2010), language learning (Connolly et al., 2011; Dunleavy et al., 2009) or medicine/health (Rosenbaum et al., 2006). In the course of our review we did not evaluate in-depth how the domain inter- acted with the design patterns and possible learning outcomes. This will be part of a more extensive analysis. Also, we did not consider possible technical or pedagogical challenges that often go together with the use of AR for learning (FitzGerald et al., 2012).
With this research, we did not intend to provide a strict one-to-one mapping of patterns to certain learning outcomes. Just as in traditional education, there is no "magic bullet" (Hattie, 2009) in the sense of one for all. Every objective goes together with an individual set of instructional approaches and techniques (Anderson, 2005). Also, educational interventions address individual groups of learners and thus learning methods need to be proved effective over and over again and if necessary, they have to be adjusted. Therefore, this research intended to offer a "display case" - like choice of options, i.e. link- ing patterns with possible learning outcomes and give examples for their implementation.
Last but not least, this chapter was based upon practical research frequently at the stage of prototypes. It thus faced the same perils than the underlying practical papers with regard to validity and reliability. Montola (2011) in his review on mixed-reality game prototypes analogically referred to problems frequently related to such settings, e.g. the fact that the introduction of a novel approach always leads to bias in an evaluation. Also, research with prototypes often uses small test audiences (even below n = 10), which makes generalization difficult.
2.7 Conclusion
This chapter’s contribution to research in the field was threefold. First, we presented an overview of current research in the field of AR and pervasive games for learning and outlined existing approaches for classifying them. Second, we introduced a framework for further analysis and appreciation of how mobile game-based learning content can support learning. The proposed framework extended an already existing framework by linking learning outcomes, patterns and context information. Five dimensions of context information were introduced that further specified the realization of the patterns Aug- mented Reality and Pervasive Games. Third, we presented results from our analysis, which showed that the link between context information and learning outcome helps to better understand the effects of individual patterns and their linkage to the process of learning. Context information added further qualitative information to the pattern and as
Classifying Pervasive Games for Learning using Game Design Patterns and Contextual Information
a result, varied the role of a pattern for mobile game design. The learning outcomes we identified were influenced by context parameters such as locations, activity and relations.
Generally, mapping learning outcomes, patterns and context information may lead to a better understanding of AR and pervasive games for learning. It can provide feas- ible results, which are suitable as a base for design guidelines that define (a) patterns, which support the achievement of a desired learning outcome and (b) ways of applying them. However, in order to better understand how mobile games support teaching and learning, further quantitative and qualitative research is needed to validate these results in regard to other design patterns for mobile games currently in existence.
Part II
Chapter 3
Developing a Mobile Game Environment to
Support Disadvantaged Learners
This chapter investigates how mobile learning games can address particular educational needs. It describes a location-based game approach to motivate learners, who are at-risk of dropping out of school, in dealing with a particular subject and a given learning content. Results from a prior target group analysis unfolded several prerequisites that needed to be considered in order to successfully pursue this objective. The literature review, initially presented in the course of this thesis, revealed patterns that seem to have strong potential to respond to the identified requirements and were considered for the concept.
This chapter is based on: Schmitz, B., Hoffman, M., Klemke, R., Klamma, R., Specht, M. (2012a). Developing a Mobile Game Environment to Support Disadvantaged Learners. Proceedings of 12th IEEE International Conference on Advanced Learning Technolo- gies (ICALT), Rome, Italy, p. 223-227.
and
Schmitz, B., Czauderna, A., Klemke, R., Specht, M. (2011). Game Based Learning for Computer Science Education. In: G. Van der Veer, P. Sloep, and M. Van Eekelen (Eds.), Proceedings of the Computer Science Education Research Conference 2011. Heerlen, The Netherlands: ACM, pp. 81-88.
Developing a Mobile Game Environment to Support Disadvantaged Learners
3.1 Introduction
Making education accessible to everyone is a problem: Besides age, the earning capacity as well as the social background influences the use of information and commu- nications technology (ICT) (Boes and Preiß ler, 2002; Zwiefka, 2007). As Unterfrauner et al. (2010) state, the smaller the income the smaller the penetration rate of PCs and the smaller the possibility for youths to have access to the Internet (no access point). Also, according to the JIM Study, the level of education influences the way, the computer is used. It is stated that the higher the educational background, the more likely youths are prepared to use the computer as a means for information. People with a lower educational background rather use the computer to communicate and to play games.
This is fortified by recent developments. People with a lower educational background are increasingly difficult to reach by formal teaching and learning. Their limited motivation to learn is often caused by negative learning experiences, a low frustration tolerance and poor stamina. Mobile learning environments seem to respond to the particular needs of learners difficult to reach. Studies have shown that mobile devices help them to recognize existing abilities and to develop and to improve confidence, autonomy and engagement (Douch et al., 2010; Pachler et al., 2010). Cook et al. (2007) emphasize that learning with mobile devices supports training measures because there is no stigma attached to carrying a mobile device around instead of a self-study manual, for example. Learners are more readily prepared to use these devices for learning because mobile devices are a cool thing to use.
Also, game-based learning scenarios are often referred to as motivating learning environ- ments that meet the younger generation’s needs (Ritterfeld et al., 2009), and particularly those of learners difficult to reach (Douch et al., 2010). Within the past decade, studies have analyzed and demonstrated that commercial as well as educational games (a) sup- port constructivist learning and teaching, i.e. constructive, situated and social learning (Gee, 2007; Prensky, 2007; Berger and Marbach, 2008) and (b) almost perfectly match the determinants of intrinsic motivation (Petko, 2008; Schiefele, 1996). According to Meier and Seufert (2003), game-based learning approaches particularly make sense if the content to be learned is dry and only somewhat interesting, if the considered target group is rather difficult to motivate for learning, if the target group already has an affinity for (computer) games (e.g. younger target groups), and/or if the target group does not have the necessary competencies to deal with a Computer Based Training (CBT) or Web Based Training (WBT), e.g. the competence to act or learn self-directed.
3.1 Introduction
Playing games, whether they are explicitly designed to foster the acquisition of know- ledge or not, may support the development of certain strategies and skills such as problem-solving, decision-making, understanding complex systems, planning or data handling. Computer games may also support the acquisition of knowledge according to a predefined set of subject-related facts (Prensky, 2007) that can be matched against a fixed syllabus. The German research project SpITKom (www.spitkom.de), for example, supported the acquisition of IT knowledge to master the European Computer Driving Licence (ECDL).
When it comes to the development of Multiplayer Browser Games, research from Social Cognition Studies is of paramount importance. The work by Steinkuehler (2004, 2008) on Massively Multiplayer Online Games, for example, emphasizes the potential of social mechanisms for learning such as collaborative problem solving practices as well as reciprocal apprenticeship "through which individuals enculturate one another into routine and valued practices and perspectives" (Steinkuehler, 2008, p. 12). According to a study carried out by Klimmt et al. (2009), gamers like Multiplayer Browser Games because of their particular social aspects of the game play and because of their specific characteristics regarding time and flexibility ("easy-in, easy-out").
The emerging concept of "gamification" also argues along these lines (Muntean, 2011). Although this concept is mainly used to describe the improvement of user experience and user engagement in digital marketing (Deterding et al., 2011), it is increasingly recognized by educational scientists. They have started to conceive it as a powerful means to make education more fun and to boost students’ interest in otherwise rather dry content (Muntean, 2011). Thus, the development of learning environments that combine mobility and gaming with learning seem to be a promising approach and first results of handheld game studies have shown positive effects on the learning outcomes (Facer et al., 2004; Shin et al., 2006). Comprehensive empirical evidence on the motivational potential and learning effects mobile game environments provide is missing though. Based on this evidence, the mobile learning game (MLG) WeBuild was developed. It was designed to further scrutinize how mobile game scenarios impact motivation and knowledge for disadvantaged learners, i.e. learners who are difficult to reach, hard to access or hard to engage (e.g third chance education).
WeBuild is based on a generic mobile learning game engine. It enables location-based and Google Maps Actions, Client/Server Communication, Multiple Choice Question Management and User Management. The combination of these features was required but not found within already existing game engines. According to Adams’ approach
Developing a Mobile Game Environment to Support Disadvantaged Learners
of player-centric game design (Adams, 2013), the user interface (UI) of WeBuild was developed in several iterative cycles with the enduser being involved in the design and development process as early as possible.
In the following, the SpITKom project and its basic technical architecture is described, which constituted the source for the WeBuild application. Subsequently, the WeBuild approach is introduced, which added to it, and the technical infrastructure is outlined. Third, the underlying mobile game engine is presented and eventually, findings from the prototype testing that was carried out in order to test interface and gameplay usability, technical functionality and motivational aspects of the game design, are reported.