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Int. J. Technology Enhanced Learning, Vol. X, No. Y, XXXX
Copyright © 200X Inderscience Enterprises Ltd.
Effects of mobile gaming patterns on learning
outcomes: a literature review
Birgit Schmitz*, Roland Klemke and
Marcus Specht
Centre for Learning Sciences and Technologies,
Open University of The Netherlands,
P.O. Box 2960, 6401 DL Heerlen,
Valkenburgerweg 177, Heerlen, The Netherlands
Email: birgit.schmitz@ou.nl
Email: roland.klemke@ou.nl
Email: marcus.specht@ou.nl
*Corresponding author
Abstract: Within the past decade, a growing number of educational scientists
have started to recognise the multifaceted potential that mobile learning games
have as a tool for learning and teaching. This paper presents a review of current
research on the topic to better understand game mechanisms with regard to
learning outcomes. The purpose of this paper is twofold. First, we introduce a
framework of analysis which is based on previous work on game design
patterns for mobile games and on learning outcomes. The framework focuses
on two aspects, motivation and knowledge gain. Second, we present a set of
patterns which we identified in the literature and that positively influence these
two aspects. Our results support the general assumption that mobile learning
games have potential to enhance motivation. It reveals that game mechnisms
such as Collaborative Actions or Augmented Reality provide incentive to get
engaged with learning and/or a certain topic. With regard to knowledge gain,
results are less comprehensive.
Keywords: mobile games; mobile learning; serious games; game design
patterns; learning outcomes.
Reference to this paper should be made as follows: Schmitz, B., Klemke, R.
and Specht, M. (20XX) ‘Effects of mobile gaming patterns on learning
outcomes: a literature review’, Int. J. Technology Enhanced Learning, Vol. x.
No. x, pp.xxx–xxx.
Biographical notes: Birgit Schmitz holds a position as a Researcher and PhD
student at the Centre for Learning Sciences and Technologies (CELSTEC) of
the Open University of the Netherlands. She is doing empirical research on the
educational potential of serious games and mobile learning games.
Additionally, she works with the Humance AG, where she manages research
projects, develops training concepts and produces multimedia trainings. She
received her diploma in Business Education in 1997 and qualified as a teacher
for vocational education and training in 2000.
Roland Klemke is an Assistant Professor at CELSTEC and works in several
European projects. His research interests are in the fields of content
development and reuse for ubiquitous use. Additionally, he is the CEO of
Humance AG and its subsidiary company Bureau 42, where he is responsible
for research and development in the product and technology area. Coordinating
the technical development team he leads industry level projects for large-scale
B. Schmitz, R. Klemke and M. Specht
customers. He received his degree in Computer Science in 1997 from
University of Kaiserslautern and a doctoral degree from RWTH Aachen in
2002.
Marcus Specht is a Professor for Advanced Learning Technologies at Centre
for Learning Sciences and Technologies at the Open University of the
Netherlands and the Director of the Learning Innovation Labs. He is currently
involved in several research projects on competence-based life-long learning,
personalised information support and contextualised and mobile learning. His
research focus is on mobile and contextualised learning technologies, learning
network services, and social and ubiquitous media for learning.
1 Introduction
Within the past five years, the number of mobile learning games (MLGs) has snowballed.
For commercial and for scientific use they have been developed for various target groups
and learning contexts (Lilly and Warnes, 2009) such as role-based history learning
(Akkerman et al., 2009), interactively discovering the principles of digital economy
(Markovic et al., 2007) or geometry (Wijers et al., 2010). Mobile learning games are
considered to have potential for encouraging both cognitive and socio-affective learning
in young adults (Mitchell, 2007). Also, Klopfer (2008) argues that mobile learning games
enable situative learning offers that make a meaningful and valuable contribution to the
process of learning by providing aspects such as temporal flexibility, natural
communication or situated learning scenarios.
The highly complex technologies and the many different gaming opportunities
available make it increasingly difficult for educational practitioners to decide which
game to choose for learning. Re-using and sharing a game is difficult without a clear and
detailed description of the benefits, targeted learning outcomes and potential impact.
There have been several efforts to find a common structure and language of games to
better understand the complex issue (Björk and Holopainen, 2004; Cook, 2010; Kelle
et al., 2011; Kiili and Ketamo, 2007). Still, there is a lack of scientifically acceptable
methodology to evaluate mobile learning games. Therefore, the purpose of this paper is
to define a conceptual framework that helps to evaluate and to categorise mobile learning
games and to identify mechanisms that support design decisions of future mobile
learning games.
Methodologically, this paper scrutinises evaluation reports on mobile learning games
(MLG). It identifies game design patterns (Davidsson et al., 2004) and analyses how
individual patterns might contribute to a particular learning outcome. Thereto, the
patterns will be lined up against Bloom’s taxonomy of educational objects (1975) within
the affective and cognitive domain (see Figure 1).
The framework might help to better understand the mechanisms of mobile learning
games and to make use of the various effects they enable. Thus, the framework raises
three questions:
How does a pattern influence learners' motivation to deal with a particular
subject or a given learning content?
What are effective mobile game design patterns to support the acquisition of
knowledge?
What are best practices for mobile learning games to support knowledge gain?
Effects of mobile gaming patterns on learning outcomes
Figure 1 Framework for the analysis
2 Analysing mobile learning games
There are a number of mobile game-based learning projects that have already tested and
evaluated the effects of mobile games on students’ learning. Only few trace their findings
back to individual game mechanisms or patterns in order to better understand why a
game is successful. Instead, reports often reason effects with the use of the game itself,
e.g. “students found the use of Lecture Quiz engaging, they perceived they learn
moreusing such games…” (Wang et al., 2008). While such statements are vital in that
they back up the more self-evident use of mobile devices for learning, they allow no
conclusions as to why and how this effect is transferrable and reproducable. In addition,
no information is deducible about what gameplay elements influence learning outcomes.
Studies often lack empirical evidence on the motivational and cognitive effects that
mobile learning games enable. However, literature provides some conclusive evidence
regarding the effects of mobile learning games which we summarise and discuss over the
course of this paper.
2.1 Basis for the analysis
We reviewed 43 empirical research articles from 2001 to 2011. We collected data from
practical projects that have already been completed and which provided information
across a broad range of domains (Zellkowitz and Wallace, 1998). Our focus was on
mobile learning games designed for teaching and learning (educational games or serious
games) with a defined learning outcome. The terms used in the search therefore included
the following keywords: mobile educational game, mobile serious game, mobile learning
game, mobile game-based learning (location-based, ubiquitous, mixed reality, augmented
reality, pervasive) learning game.
Due to the educational focus of our analysis we excluded 4 papers that had no explicit
focus on learning (Table 1, E.1), e.g. the study by Falk et al. (2001). Also, we excluded
12 studies that exclusively focused on the description of innovative technological
concepts (Table 1, E.2), such as the approaches by Ballagas and Walz (2007), Chen
(2009), Diah et al. (2010), Ferdinand et al. (2005), Mohamudally (2006), Milos et al.
(2009), Moore (2009), Martin-Dorta (2010) or Yiannoutsou (2009). For our purpose, an
explanation of the effects in relation to individual gameplay mechanisms was crucial. We
B. Schmitz, R. Klemke and M. Specht
therefore excluded 9 papers that stated evaluation results on an unspecific level with
regard to patterns (Table 1, E.3), e.g. the game contributed to increased learning and
motivation (Klopfer et al., 2011; Shin et al., 2006; Wang et al., 2008) or the use of MLGs
contributes to the development of collaboration skills (Sanchez and Olivares, 2011). We
imply that affordances of up-to-date mobile devices' hardware (e.g. accelerometer, dual
cameras, etc.) have an impact on the game and that they are reflected in the individual
design patterns composing a game. The review did not take into consideration a specific
age group. The research we reviewed was conducted mainly on pupils and young adults
(age range: 10–25 years). Possible variations in effect due to that range of age were not
considered. The following table sums up the inclusion/exclusion criteria which we
applied for the analysis.
Table 1 Inclusion and exclusion criteria for the analysis
Inclusion criteria Exclusion criteria
E.1 Reports that involved mobile games that were
not used for educational purposes.
I.1 Practical papers that reported evaluation
results from pilot studies with a mobile
learning game. Must have a clear focus on
affective and/or cognitive learning
outcomes.
E.2 Technical reports that exclusively focused on
innovation, functionality, playability and/or usability
testing.
I.2 Papers that provided comprehensive
mobile learning game design descriptions.
Must allow identification of mobile game
design patterns.
I.3 Studies that reported on concrete
learning Out comes where the learning
outcomes can be correlated with a pattern
used in the game.
I.4 Papers that are publicly available or
archived.
E.3 Papers that provided insufficient data for a
pattern -effect determination.
2.2 Theoretical framework
In order to describe the interplay and dependencies of game design patterns and learning
outcomes, we suggest a conceptual framework which comprises two components:
1 The game design patterns for mobile games established by Davidsson et al. (2004)
2 The taxonomy of learning outcomes established by Bloom (1956)
On the one hand, the analysis was carried out on the basis of the patterns described by
Davidsson et al. (2004). As an advancement to the work of Björk and Holopainen (2004),
who established an initial set of more than 200 game design patterns for computer games,
the approach by Davidsson et al. describes gameplay mechanics of mobile games. The
patterns provide a common language for industry and academia and help describe the
rapidly developing area of mobile games. Each pattern is identified by a a core definition,
a general definition, example(s), descriptions of how to use the pattern (by listing related
patterns or patterns that can be linked to it), the description of its consequences, relations
with regard to instantiation (patterns causing each other’s presence) and modulation
(patterns influencing each other), as well as references.
Effects of mobile gaming patterns on learning outcomes
The pattern Physical Navigation, for example, “forces players of a mobile game to
move or turn around in the physical world in order to successfully play the game”
(Davidsson et al., 2004, p.18). The MLG Frequentie 1550 (Akkerman et al., 2009), for
instance, uses this pattern. Players have to move around to find sources of information
and to complete tasks. Also, Explore (Costabile et al., 2008) makes use of this pattern. It
requires groups to walk around the ruins trying to identify the place the mission refers to.
The pattern Physical Navigation is instantiated by (caused by the use of), e.g. the
pattern Player-Player Proximity, Player-Artifact Proximity, Player-Location Proximity
and Artifact-Artifact Proximity. The pattern Player-Location Proximity in turn is defined
by the distance between the player and a certain physical location which can affect
gameplay and trigger an event. Frequentie 1550 makes use of this pattern. On entering
one of the six areas the old city of Amsterdam is divided into (each area dealing with a
different theme in medieval times), an introductory video clip is provided. The video clip
presents words that can help to complete the assignments in that area (Akkerman et al.,
2009).
On the other hand, we classified the effects extricated from the empirical studies
according to learning outcomes. A learning outcome is the specification of what the
successful learner is expected to be able to do at the end of the module/course unit or
qualification (Adam, 2004). Learning outcome orientation can be seen within a wider
trend in educational technology. One of its main ideas is to prepare students for the
requirements of professional life (Vander Ark, 2002). Rather than defining the resources
to be used during the learning process, outcome-oriented learning scenarios focus on the
results of the educational process, e.g. the skills and content students are able to
demonstrate. To depict the various learning outcomes, we applied Bloom's taxonomy
(1956) which sorts learning outcomes into three domains:
affective domain – motivational learning outcomes
cognitive domain – knowledge learning outcomes
psychomotor domain – manual/physical learning outcomes
According to Bloom, the affective domain encompasses attitudes and motivation. The
cognitive domain deals with the recall or recognition of knowledge and the development
of intellectual abilities and skills. The psychomotor domain encompasses manual or
physical skills or the performance of actions. For the review we focused on motivational
and knowledge learning outcomes. Learning outcomes that relate to manual or physical
learning outcomes, e.g. exergames (cf. Lucht, 2010; Yang, 2011) or console games were
not considered, as they have a different didactic approach.
For the cognitive domain, Bloom distinguishes six successive levels that can be
fostered – Knowledge (e.g. observation and recall of information, knowledge of dates,
events and places), Comprehension (understanding information, grasping meanings or
ordering, grouping, inferring causes), Application (using learned material in new
situations, putting ideas and concepts to work in solving problems), Analysis (breaking
down information into its components, understanding organisational structure), Synthesis
(putting parts together) and Evaluation (judging the value of material for a given
purpose).
The framework described above provides the basis for categorising gameplay
mechanics according to learning outcomes. From this categorisation we expect to enable
B. Schmitz, R. Klemke and M. Specht
a rather specific use of gameplay elements. We aim at defining which patterns support
(a) motivational learning outcomes and (b) cognitive learning outcomes in the six
categories from least complex (knowledge) to most complex (evaluation).
3 Results of the analysis
In the following section, we present the results of the literature survey. For the review,
we searched practical papers regardless of any particular pattern. In a first step, we
scrutinised what games impact motivation (affective learning outcomes) and knowledge
(cognitive learning outcomes). We then went into detail, focusing on the patterns used in
the games. We listed the patterns and investigated how individual patterns impact
motivation or knowledge.
The mobile learning game ARGuing, for example, impacts both affective and
cognitive learning outcomes. From the study by Conolly et al. (2011), we identified the
following patterns: Pervasive Games, Collaborative Actions, Cooperation, Communication
Channels, Competition, Imperfect Information, Memorability and Avatar. For the pattern
Pervasive Games, we were able to extricate effects with regard to motivational and
cognitive learning outcomes (see Tables 2 and 3). The pattern Pervasive Games for
example, impacts affective learning outcomes: Learners are motivated to learn a foreign
language. The following sections list our findings from reviewing the literature by
patterns and present the effects we identified.
3.1 Affective learning outcomes
The literature found in the course of this review indicates that MLGs have strong
motivational effects. In traditional instructional design, the concept of motivation is vital
for the process of learning: In order to initialise learning and subsequently to successfully
process knowledge, motivation is crucial (Klauer, 2007). In the course of our analysis,
we identified several patterns that positively influence motivation both in terms of fun as
well as getting engaged with a learning environment or a certain topic to develop
intellectual abilities and skills. Table 2 lists these patterns, describes them and presents
their effects. The descriptions are taken from the pattern lists by Davidsson et al. (2004)
and Björk and Holopainen (2004).
Table 2 Effects of patterns with regard to affective learning outcomes
Pattern Pattern description Affective learning outcome
Collaborative
Actions
Two or more players being at
the same location at the same
time or attacking a target
simultaneously.
Students are engaged in the game (Costabile et al.,
2008, Dunleavy et al., 2009, Liu et al., 2010,
Rosenbaum et al., 2006).
Students exchange and discuss game progress
(Klopfer and Squire, 2007).
Cooperation Players are forced to work
together in order to progress in
the game.
Participants are driven by a good team spirit
(Costabile et al., 2008).
Players interact more with the object of learning
(Sedano et al., 2007).
Social
Interaction
Players have the possibility to
meet face to face.
Students are engaged in discussion (Klopfer and
Squire, 2007).
Effects of mobile gaming patterns on learning outcomes
Table 2 Effects of patterns with regard to affective learning outcomes (continued)
Pattern Pattern description Affective learning outcome
Students feel “personally embodied” in the game.
Their actions in the game are intrinsically
motivated (Rosenbaum et al., 2006).
Augmented
Reality (AR)
Players’ perception of the game
world is created by augmenting
their perception of the real
world. Learners are engaged and motivated to learn and
use foreign languages (Conolly et al., 2011).
Learners are attentive (Wijers et al., 2010).
Students are mentally ready for learning (Schwabe
and Göth, 2005).
Players immerse themselves in the game (Carrigy
et al., 2010).
Participants are exceptionally activated (Markovic
et al., 2007).
Learners are motivated to play the game (Conolly
et al., 2011).
Pervasive
Games
Play sessions coexists with
other activities, either
temporally or spatially.
Students’ attitude towards learning material
improves (Markovic et al., 2007).
Students are highly motivated (Dunleavy et al.,
2009).
Participants are interested and moved (Schwabe
and Göth, 2005).
Physical
Navigation
Players have to move or turn
around in the physical world in
order to successfully play the
game.
Students’s are exited (Facer et al., 2004).
Perfect
Information
The player has full and reliable
access to information about a
game component.
Students are engaged in the game (Admiraal et al.,
2011).
Predefined
Goals
The goal is explicitly or
implicitly stated when the game
starts. When the goal is
fulfilled, the game is over.
Students are engaged in the game (Admiraal et al.,
2011).
Extra Game
Information
Information is provided within
the game that concerns subjects
outside the game world.
Players are curious and interested in the game
(Sedano et al., 2007).
Participants are eager to finish the game (Sedano
et al., 2007).
Imperfect
Information
One aspect of information
about the total game situation is
not fully known to a player. Players immersed in the narrative (Carrigy et al.,
2010)
Score Numerical representation of the
player’s success in the game,
often also defining it.
Students are motivated to deal with content on a
regular basis, positive peer pressure (Douch,
Attewell, and Dawson, 2010).
Agents Entities controlled by the game
system, e.g. to support narrative
structure.
Students are motivated to deal with the learning
material (Liu et al., 2010).
The patterns Avatar, Competition and Roleplaying are not part of the revised list by
Davidsson et al. (2004). They are part of the original list of Game Design Patterns
B. Schmitz, R. Klemke and M. Specht
provided by Björk and Holopainen (2004). However, the patterns seemed to be relevant
for the design of mobile learning games too. We therefore included them in the study
(Table 3).
Table 3 Effects of patterns with regard to affective learning outcomes
Pattern Pattern description Affective learning outcome
Avatar Game element which is tightly
connected to the player’s success
and failure in the game.
Students identify with the game characters
(Winkler et al., 2008).
Competition Struggle between players or
against the game system to
achieve a certain goal where
performance can be measured.
Students are engaged in the game (Wijers et al.,
2010).
Students are focused and attentive (Admiraal et
al., 2011).
Roleplaying Players have characters with at
least somewhat fleshed out
personalities. The play is centred
on making decisions on how
these characters would take
actions in staged imaginary
situations.
Learners are involved in the game (Facer et al.,
2004).
Students feel highly engaged and identify with
their roles in the game (Facer et al., 2004,
Costabile et al., 2008).
Students merge with the game (Rosenbaum
et al., 2006).
Learners are tightly associated with their tasks in
the game (Rosenbaum et al., 2006, Wijers et al.,
2010).
Students take on an identity. They are eager to
work together (Dunleavy et al., 2009). Learners
felt rewarded and engaged in the game (Carrigy
et al., 2010).
From the empirical studies we could ascertain that mobile learning games can help (a) to
increase learners’ motivation to engage with a particular learning environment, in our
case this is to play the learning game (Admiraal et al., 2011; Costabile et al., 2008;
Rosenbaum et al., 2006; Sedano et al., 2007) and (b) to foster students’ motivation to
engage in learning activities and to deal with a particular learning content (Douch et al.,
2010; Markovic et al., 2007; Schwabe and Göth, 2005). In particular patterns such as
Cooperation, Augmented Reality, Pervasive Games or Physical Navigation seem to
positively influence learners' motivation to deal with a particular subject or a given
learning content.
3.2 Cognitive learning outcomes
With regard to ‘hard learning’ (Schwabe and Göth, 2005), it seems that very often the
assumed positive effect of MLGs on cognitive learning outcomes cannnot be
substantiated. Only few studies report traceable distinctions between learning with a
mobile device and learning with rather traditional instruction (e.g. regular lessons).
However, some of the evaluations report on positive interrelations between learning with
a mobile game and cognitive learning outcomes. In the course of our review we scanned
the game descriptions and game evaluations for patterns that may cause such positive
interrelations. The following table presents the results. For the cognitive learning
outcomes, we formulated the results in line with the verbs Bloom considered as suitable
Effects of mobile gaming patterns on learning outcomes
for describing the several levels in written objectives. Table 4 lists the relevant patterns
and describes their assigned cognitive learning outcomes. Since Table 4 contains the
same patterns than Table 3, the pattern descriptions apply accordingly.
Table 4 Effects of patterns with regard to cognitive learning outcomes
Pattern Cognitive learning outcome
Collaborative
Actions
Cooperation
Students memorise their knowledge Collaborative (Winkler et al., 2008)
Students can explain and rewrite the knowledge learned (Liu et al., 2010).
Social
Interaction
Students are able to scientifically argument (Klopfer and Squire, 2007).
They can rewrite the knowledge learned (Liu et al., 2010).
Competition Students can memorise the material learned (receive higher scores on
the knowledge test) (Admiraal et al., 2011, Huizenga et al., 2009).
Augmented
Reality (AR)
Students notice and discuss geometrical aspects of the world
(Wijers et al., 2010).
They can describe and illustrate a disease model (Rosenbaum et al., 2006).
Students reflect on the process of learning (Costabile et al., 2008).
Pervasive Games Students can recall the learned material (Akkerman et al., 2009).
Learners are able to transfer the learned material (practical knowledge and
practical experience) (Markovic et al., 2007).
Students reflect on their learning. They can solve problems related to the
object of learning. They can create new problems related to the object
of learning They can judge and evaluate the material for a given purpose –
critical thinking skills. They are able to analyse and classify the learned
material (Conolly et al., 2011).
Extra-Game
Information
Students can rewrite the knowledge learned (Liu et al., 2010).
Roleplaying Students can give examples for the importance of communication and
collaboration (Rosenbaum et al., 2006).
The review revealed that only few studies empirically research the actual cognitive
learning outcomes from MLGs (e.g. pre-test/post-test). Papers discuss the educational
value of diverse patterns but provide little evidence that this approach leads to better
learning outcomes. On the one hand, this is due to the fact that patterns have only to a
limited extent been subject to explicit reasearch. But on the other hand, studies seldom
explicitly research the cognitive learning outcomes of MLGs. Many pilot studies apply
qualitative measurements to evaluate effects. Further research is needed to provide a
clearer picture of how individual patterns or groups of patterns function and how they
effectuate cognitive learning outcomes. In order to provide an in-depth understanding of
the educational effects of game design patterns for MLGs, we suggest a mixed methods
evaluation (Pérez-Sanagustin et al., 2012), which combines quantitative and qualitative
data.
4 Conclusion and discussion
In this paper we have presented the findings from our review of practical research papers
on the effects of mobile learning games. It indicates that mobile learning games have the
B. Schmitz, R. Klemke and M. Specht
potential to bring about affective as well as cognitive learning outcomes. MLGs can help
to increase the motivation to engage in learning activities. With regard to “hard learning”
(Schwabe and Göth, 2005) though, empirical evidence is fragmented. In general, the
empirical evidence in the literature we reviewed was inconsistent in terms of study
design and terminology. The diverse studies had different settings with regard to
the statistical base (dependent/independent variables) and the research methods they
applied, as they addressed various research interests. Still, some verifiable effects are
in existence.
For both, affective and cognitive learning outcomes, it showed that, firstly, the impact
of individual patterns on learning is difficult to determine. The studies we reviewed
focused on a set of diverse patterns, which is given by definition. The use of one pattern
mostly requires the presence of another game design patterns (Björk and Holopainen,
2004; Klemke, and Specht, 2012). From this, other complexities derive: Does a pattern
on its own have the same effect or does it require interplay with other (particular)
patterns? For example, it was stated that the provision for the pattern Competition
positively influenced students, learning (Akkerman et al., 2009). The game additionally
provided for the patterns of Team Play, Score and Cooperation, which had an impact on
the competition between the groups too. Also, the affordances of the mobile devices'
hardware have an impact on the pattern employed by a designer. We implied that the
diverse patterns already reflect the technical possibilities.
Secondly, the effects occured with a given condition of the patterns, e.g. given time,
given level, etc. To what extent does varying the conditions of the diverse patterns (game
balancing) influence the effect? For example, the provision of Imperfect Information was
identified to motivate learners to finish the game. What amount of information is
necessary in order not to overstrain (discourage) or bore the learner?
In order to reduce such complexities in the pattern approach, further research on the
correlations between patterns and learning outcomes has to focus on a limited number of
the patterns in existence (Björk and Holopainen, 2004; Davidsson et al., 2004). The study
settings have to comprise (a) an experimental variation of patterns, i.e. game settings that
enable/disable individual patterns and (b) an in-depth variation of patterns, i.e. game
settings that allow different instances for the same pattern. This way, measurable and
feasible results can be obtained that are suitable as a base for design guidelines which
define (a) patterns that support the achievement of a desired learning outcome and (b)
ways of applying the patterns.
Game design needs to adapt to different target groups, contexts, etc. (Adams, 2010).
This in particular applies to the context of educational games. There is a vital need for
tailoring learning offers (i.e. educational games) to learners’ needs, capabilities and
according to learning targets. Intelligent adaptive game mechanisms generally reflect this
need. To a certain degree, this also applies to the patterns Level or Score. This way, the
pattern approach reflects varying target groups or contexts. A more specific analysis, e.g.
the extend to which individual patterns reflect learners’ needs or capabilities, is needed
though. Future reseach needs to verify the effectiveness of mobile learning games and to
corroborate their educational value in order to motivate teachers to use such tools for
teaching. Otherwise, the educational system may run the risk of disengaging future
learners (cf. Klopfer et al., 2011).
Effects of mobile gaming patterns on learning outcomes
5 Further work
From what was mentioned above it becomes obvious that there is clearly a need for more
comprehensive scientific studies that scrutinise the functions of the diverse patterns
mobile learning games are based on. The main research question we need to address is
therefore:
How can an effective mobile learning game be developed that enhances
motivation and cognitive learning outcomes?
The framework focuses on two aspects: affective and cognitive learning outcomes. As for
the affective learning outcomes, we identified patterns that positively impact
motivational aspects. Future research will have to investigate:
How does a pattern or a group of patterns, e.g. the provision for Competition,
influence the learners' motivation to actually deal with a particular subject or
a given learning content?
For our research, we have to consider groups of patterns because learners seldom
perceive single patterns as a game (Kelle et al., 2012).
Also, the study results show a small, though positive correlation between diverse
patterns and cognitive learning outcomes. With respect to knowledge gain, this PhD-
work will further investigate:
To what degree does a particular pattern, e.g. Player Physical Prowess,
increase the learner’s knowledge gain?
Will pupils playing mobile learning games that provide for a particular pattern
have better knowledge gains than pupils receiving traditional lesson series?
A comprehensive evaluation is to follow which examines the research questions stated. It
seeks to understand which specific patterns have the greatest impact on a stated learning
outcome. Also, the degree of effects will be the subject of future studies, for example the
degree of motivational effects of individual patterns, e.g. intrinsic versus extrinsic
motivation (cf. Schiefele and Schreyer, 1994), as well as influencing variables such as
age or the prevailing level of education (i.e. educationally disadvantaged learners).
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