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The Elektra Project: Towards a New Learning Experience.

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Abstract Digital game-based ,learning is a ,hot topic of research and development. Since the advent of computer and video games, educators were inherently interested in utilizing the beneficial aspects of computer games for educational purposes. These factors are primarily the intrinsic motivation of games, immersive environments, engaging stories, and an artful balance between challenges and continuously,growing ,abilities. Proponents of computer ,games ,delivered a large ,number ,of empirical investigations revealing that games may ,foster the development ,of abilities ,and competencies. Besides the advantageous aspects of computer games, a variety of problems were reported by researchers. Due to the high costs of professional game development, many educational games ,are technologically poor and cannot compete ,with entertainment ,games ,in terms of visual design, possibilities for interactions, or storytelling. Moreover, many current educational games do not incorporate a sound psychological, pedagogical, or didactic background; instead they are focusing on transmission ,or rehearsal ,of isolated ,facts or skills. Finally, such games lack the ability to adapt to individual competencies failing to balance challenge and abilities regarding knowledge or skills. The ELEKTRA project, introduced in this article, aims for addressing these problems relying on an interdisciplinary approach of cognitive science, neuroscience, pedagogy, game design, and game development. The project will develop an adventure,game ,that can ,keep ,up with ,commercial games ,and ,that focuses on primarily curriculum-related educational,purposes by incorporating a sound,psychological and pedagogical framework. Moreover, the project will prove the outcomes of research and development by a comprehensive,game demonstrator.
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THE ELEKTRA PROJECT:
TOWARDS A NEW LEARNING EXPERIENCE
Michael D. Kickmeier-Rust1, Daniel Schwarz2, Dietrich Albert1,
Dominique Verpoorten3, Jean-Loup Castaigne3, Matthias Bopp4
Abstract
Digital game-based learning is a hot topic of research and development. Since the advent of
computer and video games, educators were inherently interested in utilizing the beneficial aspects
of computer games for educational purposes. These factors are primarily the intrinsic motivation of
games, immersive environments, engaging stories, and an artful balance between challenges and
continuously growing abilities. Proponents of computer games delivered a large number of
empirical investigations revealing that games may foster the development of abilities and
competencies. Besides the advantageous aspects of computer games, a variety of problems were
reported by researchers. Due to the high costs of professional game development, many
educational games are technologically poor and cannot compete with entertainment games in
terms of visual design, possibilities for interactions, or storytelling. Moreover, many current
educational games do not incorporate a sound psychological, pedagogical, or didactic
background; instead they are focusing on transmission or rehearsal of isolated facts or skills.
Finally, such games lack the ability to adapt to individual competencies failing to balance
challenge and abilities regarding knowledge or skills. The ELEKTRA project, introduced in this
article, aims for addressing these problems relying on an interdisciplinary approach of cognitive
science, neuroscience, pedagogy, game design, and game development. The project will develop an
adventure game that can keep up with commercial games and that focuses on primarily
curriculum-related educational purposes by incorporating a sound psychological and pedagogical
framework. Moreover, the project will prove the outcomes of research and development by a
comprehensive game demonstrator.
1. Introduction
The idea of using computer games for educational purposes was already born with the appearance
of the first computer games. In each year since the advent of computer games, scientists and
developers have published numerous articles and books on the advantages of digital game-based
learning [79]. This idea, however, caused considerable debate regarding the power of games for
educational purposes, the advantages, disadvantages, costs, and risks. At the same time, computer
game industry constantly increased sales up to seven billion US$ in 2005 [30]. Estimations say that
more than 100 million people frequently play computer games, alone in the US [56]. A significant
number of young people spend many hours a week playing computer games [6][79] and most often
these games are the preferred play [78]. Thus, the attempt to utilize gaming activities and benefits
1 Cognitive Science Section, Department of Psychology, University of Graz, Austria
2 Laboratory for Mixed Realities (LMR), Cologne, Germany
3 Support Laboratory for Telematic Learning (LabSET), University of Liège, Belgium
4 Center of Advanced Imaging (CAI), University of Bremen, Germany
of games for educational purposes seems to be a promising approach to improve and facilitate
learning, especially when fun, motivation, and immersion could be maintained. Most concisely,
games can contribute to educational aims in four ways: (a) the time spent for gaming could be
utilized -at least partially- for learning, (b) the intrinsic motivation, engagement, and immersion of
computer games could be utilized to make learning a more pleasant task and support problem-
oriented learning with its challenges driven by game-play, storyline and user interaction, (c) the
augmentation of both traditional and interactive storytelling in current adventure games may be
also utilized for learning, since storytelling is maybe the most natural teaching and learning
method, and (d) the rich visual environment of current games could be utilized to present new and
unusual perspectives within meaningful contexts.
Facing the broad range of possibilities, since the nineteen nineties research and development
increasingly focused on the effects of playing computer games for entertainment on a variety of
abilities, behaviors, or psycho-social issues. Additionally, more and more games have been
developed primarily aiming for educational goals. Research and developed covered the whole
range of game types, from pure entertainment to so-called edutainment to pure education. The
results of research are widely promising; reviews and meta-analyses constantly found that games
are able to promote learning [56][79]. Skeptics, however, argued for the difficulty of measuring
complex variables or constructs and the need to narrowly define variables and tightly control
conditions. Such research most often leads to studies that make correspondingly narrow claims
about tightly controlled aspects of games (cf. [79]). Additionally, major weaknesses of games used
for educational purposes were reported, especially when aiming to utilize games for teaching in
classrooms and related to curricula [32][17][10].
1.1. Games for entertainment
Over the past decades a considerable amount of research has been done on the impact of
entertainment games on a variety of psychological and sociological variables and constructs. This
research covered the entire spectrum of game types and revealed positive as well as negative effects
of (frequent) gaming.
Self-esteem, as one example, was found to be raised by the amount of control and mastery
offered by games. However, there is some evidence that these games become a substitute for social
interactions and relationships because of the increase of self-esteem imparted by games
[15][72][19]. Contrarily, studies revealed that frequent gaming does not significantly influence
social behavior and relationships [35][18]. Also more recent studies could not clarify these
contradictory results. Subrahmanyam [77] and colleagues suggested that more research has to be
done in this direction.
Violence and anti-social behavior, integral parts of many entertainment games, were also
subject of research and frequently initiated an ongoing debate about the negative influence on
violent behavior of gamers [6][7][9][11]. Generally, violent games are viewed as intensifying factor
for violent or anti-social tendencies; games might blur young people’s perceptions of the difference
between real life and virtual reality, to the extent that they become desensitized to aggression and
violence [77].
Also the effects of computer games for learning and academic performance have been
investigated in-depth. Generally, frequent gaming is assumed to foster the ability to adjust to a
computer-oriented knowledge and information society [36] and computer skills. Moreover,
computer games are able to influence gamers’ strategic and inferential thinking [68] and also the
ability to create mental images of information structures [66]. Other beneficial aspects of playing
computer games were found, for example, in meta-cognition, spatial abilities, iconic memory,
psycho-motor skills, visual attention, peripheral vision, mental rotation abilities, decision making,
or even improvements of memory [24][34][44][45][69][78].
However, most often these studies focused on narrowly defined aspects of games (cf. [79]).
Symptomatic for this kind of research is that improvements in certain constructs often perfectly
matched the demands of specific games (e.g., mental rotation skills when playing hours of Tetris
[60][28]). Thus, generalizations to computer games or types of computer games are difficult.
Moreover, interpretations of experimental results are often biased towards favoring games. As an
example, experiments [42] revealed statistically significant increase in the participants’
“multitasking abilities” when playing 3D games. However, this author did not discuss the fact that
this result might also be interpreted in terms of a reduction of gamers’ abilities to concentrate on
one thing at a time.
In summary, even if research results are not perfectly clear, there is some evidence, that
playing computer games are able to improve desirable factors like computer skills or different
cognitive abilities. The use of games in classrooms, moreover, is fostering motivation and attention
[73]. On the other hand, games do have an addictive potential and frequently playing might
negatively impact social abilities and relationships. Concerning planned and maybe curriculum-
based education, existing games for entertainment have a very limited potential. Games must be
aligned with the curriculum, didactic strategy, and content. According to [79], the latter is most
difficult because games for entertainment tend to neglect accuracy and completeness of contents.
1.2. Games for education
In consideration of the pervasive influence of computer games on our culture and the amount of
time gamers spend on playing computer games, many educators, psychologists, and game
developers have increasingly focused on investigating and directly incorporating the beneficial
aspects of computer games for educational purposes. This is a great attempt because, even if some
educators seriously argue against “making learning fun” [76], playing games is one of the most
natural forms of learning. Children start learning to talk by playing with noises, they acquire motor
skills by pushing coins into a piggy bank, or they learn collaboration and strategic thinking when
playing Cowboy and Indian. Accordingly large is the body of available computer games primarily
designed for educational purposes.
Game-based approaches were used, for example, to support children learning specific skills
such as reading or math [44][74]. Halttunen and Sormunen [37] developed an information retrieval
game or Kusunoki and colleagues [49] designed a game teaching urban planning and environmental
issues, supporting factual learning, and, used collaboratively, enhancing cooperation and the ability
to discover how others think. Of course also simulation games have found a wide application and
are amongst the most effective and successful approaches of using games in education. Besides
typical application of simulations in adult training (e.g. in pilot training or in military training in
order to reduce risks, costs and in order to realize situation which cannot be realized in reality) such
approaches have been also used in classes, e.g. for improving cognitive abilities and factual
learning in certain subjects [22][38].
The list of such games could be extended further on [21] (Figure 1): British Gas created a
game illustrating the importance of customer communication and diagnostic problem solving,
INTEL created a game illustrating the importance of IT security, PIXELearning created a game to
foster the development of skills for retail staff, Games2Train created a game to teach the use of 3D
CAD software, the German association Schulen ans Netz e.V. established an online platform for
girls including games like job application trainings, The European project UniGame
(http://www.unigame.net) introduced the game-based learning concept to higher education and
lifelong learning.
Generally, games with primarily educational proposes were used to practice certain skills
and to improve awareness but less for teaching factual knowledge. Moreover; most existing games
have a clearly limited scope, due to the high costs of developing professional games they are often
simple and limited in providing intrinsic motivation, immersion, and engagement. Even if the
examples shown in Figure 1 are state-of-the-art examples of game-based learning, they cannot
compete with commercial games. Finally there is a lack of empirical research investigating the
impact of such games on learning performance and comparing the games’ success with other
methods of teaching and learning.
Figure 1. Examples of current game-based learning. The left images displays a 3D
office simulation game by PIXELearning, the right a quiz game by Games2Train.
1.3. Advantages and disadvantages of computer games in education
The main advantages of using computer games for educational purposes are intrinsic motivation
and the technological possibilities of virtual environments. Computer games are intrinsically
motivating, pervasive, and gamers voluntarily spend a significant amount of time for playing
computer games. Games are played to achieve a certain goal or, in other terms, to win. Thus,
motivation is based on “winning while remaining challenged” [10]. Moreover, computer games
enable the creation of virtual learning spaces within which new perspectives on a subject (e.g.,
viewing or manipulating an atom) can be realized and learning content can be presented to the
learner in a meaningful context. Digital learning environments can benefit from the wide
experience of modern computer games by providing holistic learning experience – since pure
information does not become knowledge until it is imparted within a context and can therewith be
experienced. However, the mystery of these games is closely related to learning: it is essential to
meet a challenge and a task, to solve problems, to improve and of course to be awarded. One plays
a game in order to win. This is precisely how the human brain can be motivated to learn: We want
to understand ourselves, explore and understand the world around us and rise the challenge – if we
succeed, we obtain comprehension and self-confidence. And we want to discover and know even
more.
The question is which factors (visual environments, storylines, challenges, interactions with
non-player characters) are responsible for these advantages and what are the general rules to
incorporate them with future educational computer games.
Malone [53] outlined three factors that “make video games fun”: Challenge, fantasy, and
curiosity. He used these concepts to outline several guidelines for creating enjoyable education
programs:
clear goals that students find meaningful,
multiple goal structures and scoring to give students feedback on their progress,
multiple difficulty levels to adjust the game difficulty to learner skill,
random elements of surprise, and
an emotionally appealing fantasy and metaphor that is related to game skills.
Prensky [71] has outlined similar key factors, which contribute to the immersive capabilities of
computer games: clear rules, continuous challenge and competition, clear goals and objectives,
direct and instant feedback, and an immersive storyline. Moreover, the rich virtual environments of
3D games might stimulate fantasy and involve the gamer in a virtual world [70] and ambiance
information create a rich learning experience [71].
In a pure educational study of factors influencing students motivation Viau [80] emphasizes
that the 3 factors (Figure 2) that can be influenced are the learner’s perception regarding (a) the
value of the activity, (b) his/her competence to fulfill the activity and (c) the control he/she has on
the activity. An obvious relation can be made between Malone’s [53] clear goals that students find
meaningful and Viau’s perception regarding the value of the activity. Viau’s factor about the
competence is linked to Malone’s and Prensky’s challenge concept especially when the difficulty
level is to be adjusted to the learner’s level. The challenge is not to be too easy or too difficult
otherwise the player motivation will decrease. Viau’s control factor is clearly linked to the amount
of control and mastery offered by games and self-esteem (see paragraph 1.1).
Figure 2. Motivational dynamic of a learner translated and documented from Viau [80].
The main disadvantages are that educational games require a certain balance between learning and
gaming action to maintain fun, motivation, or even flow, they require a balance between challenge
and ability, and the development of immersive games is expensive.
The balance between actions related to learning and actions related to fun and maintaining
motivation is a crucial factor for a cost-benefit trade-off in educational games. It might be difficult
to find this balance for all individuals of a target group. In terms of effective and efficient learning
it would be inadequate to design an educational game that is highly motivating and engaging and,
on the other hand, that is limited to only a small amount of learning content. The risk is high that
what can be learned from one textbook page within 10 minutes could take significantly longer to be
learned with a computer game which must be enriched with an engaging storyline. In turn,
educational software might be disliked by students because of a lack of fun [52].
Furthermore, effective games require a continuous balance between challenge and ability.
While game designers have a long tradition and experience in realizing such balance for gaming
actions, e.g. battling an opponent, this is not a trivial task for realizing it in terms of knowledge,
skills, or competencies. Consequently an adaptive approach is required that accomplishes such
balance and that, again, requires an appropriate learner model.
Clark [17] has highlighted a number of additional risk factors that might have negative
effects on learning with computer games:
Games may distract from learning when the gamers primarily concentrates on game
actions, e.g. discovering the virtual environment or battling opponents,
males and females might not be reached by certain types of computer games to the
same extent, or
the risk of “hijacking” the virtual word by educators.
Finally, the creation of professional computer games is very costly [32]. This is true in
terms of an appealing design as well as in terms of implementing appropriate didactic methods.
Current educational games often confine to a single, traditional learning method, such as reading or
listening. In this regard, such games do not differ from other multi-media learning applications.
Often the taught as well as the tested knowledge is nothing else but a collection of isolated facts or
skills. Learning mainly proceeds in a linear fashion, which means that the learner is confronted
with a predefined sequence of learning situations. Moreover, many learning games do not test and
profile learners before they enter the game world, and they are generally not able to pay attention to
individual performance or learning progress. Additionally, the design of many educational games is
not appealing or engaging. They generally do not meet gamers’ demands on 3D learning
environments and graphics as they are state-of-the-art in commercial computer games. The same is
true for gaming possibilities or storylines. Consequently, many current learning games
underperform in motivating the learner. They do not use realistic non-player characters that have a
convincing appearance and mimic, and maintain eye contact, which is an important factor for
motivating the player and is standard in non-learning games. Finally, they do not make use of the
motivating power of elaborated story telling.
Summarizing, a large number of today’s educational games in contrast to current
commercial computer games do not engage learners as they suffer from distractive and incoherent
learning and gaming experience, low interactivity, linear learning paths, poor storytelling, poor
graphics, weak adaptivity features, and not account for a learner’s degree of confidence.
Also [10] reports similar disadvantages of games: It’s easy to address the wrong level of
interest and challenge for a learner, games might be to easy or too difficult for a learner, it might
take a long time to work through a game, due to high costs of professional game development,
educational games might easily be poorly designed, games can be addictive, and games might
include gender biases and stereotyped characters.
The question is how these disadvantages can be addressed in order to enable a more efficient and
effective use of computer games for education. Playing games, as mentioned, is one of the most
natural forms of learning. This form is in accordance with constructive approaches to learning.
Such cognitive or constructivist approaches to learning claim that information per se is not
knowledge; knowledge is constructed by cognitive processing of information and by integrating
new information into existing knowledge. The knowledge construction process relates to the
learner not as an empty vessel into which pre-formulated knowledge can be poured undigested, but
someone with an existing knowledge structure onto which they must fit their new learning. A
constructivist approach will therefore begin with what the learner already knows and involve them
in active engagement with the new [55]. Computer games offer a wide range of possibilities to
“construct” new knowledge in a meaningful context and allow a persistent active engagement
which might give games the edge over traditional transmission of information.
2. ELEKTRA: Enhanced learning experience and knowledge transfer
The acronym ELEKTRA (www.elektra-project.org) stands for “enhanced learning experience and
knowledge transfer”. ELEKTRA is a two-year research and development project funded by the
European Commission under the Sixth Framework Programme in the IST research priority.
ELEKTRA has the ambitious and visionary goal to fully utilize the advantages of computer games
and their design fundamentals for educational purposes and to address and eliminate the
disadvantages of game-based learning as far as possible. Nine interdisciplinary European partners
contribute to development and research in order to develop a methodology for designing
educational games as well as a comprehensive game demonstrator. Furthermore, important research
questions shall be addressed by the project both on the level of the involved research domains and
on an interdisciplinary level. The consortium members are:
Laboratory for Mixed Realities (LMR, Germany) is the project coordinator and also
responsible for interdisciplinary and overall conception, interactive information design
and narration, game design, character design and development
Testaluna (Italy) is responsible for game design and development
KLETT Lernen und Wissen GmbH (Germany) is responsible for content development
and for game publishing
Broadview TV (Germany) is responsible for media production and dissemination, as
well as production of game assets and animations
ORT France is responsible for evaluation and validation of the game demonstrator,
game components and instructional design
Trinity College Dublin (Ireland) is responsible for the technological development of
adaptive learning and teaching within the game
University of Liège (Belgium) is responsible for pedagogical implications, didactic
design and curriculum development
Center of Advanced Imaging (CAI) of the University of Bremen (Germany) is
responsible for neuroscientific research and media pedagogy
University of Graz (Austria) is responsible for adaptive user modeling and cognitive
research on game-based learning
2.1. The ambitious vision of ELEKTRA
The aim of the ELEKTRA project is to establish a methodology which incorporates the advantages
of computer games for educational purposes and to overcome disadvantages and potential risks.
Modern computer games let their players enter the virtual worlds by offering a high degree of
interactivity and realism. They turn the user into the active protagonist and hero, who can act and
interact using more and more complex game features. Furthermore, the “gamer” is embedded in a
fascinating universe having its own rules and residents to be discovered. By the player’s own
actions and his involvement into the framework story the game becomes important and meaningful
to him. ELEKTRA will build upon these success principles and apply their design and realization
in current computer games in order to develop new forms of didactic design for game-based
learning. The aim is to excite and motivate learners equally as leading edge commercial games
excite gamers and improve the knowledge transfer at the same time. Learning situations will be
presented in a rich and immersive virtual 3D environment offering traditional and new perspectives
to learning content and embedded in a meaningful and motivating storyline. This will offer both a
consistent world experience that fosters contextual learning and envision knowledge in imaginative
and visionary stunning ways. Moreover, ELEKTRA will incorporate sound pedagogical and
psychological learner models and didactics. Within this learning game environment ELEKTRA
fosters a user-centric approach as regard to both learning and gaming by enabling constructivist and
active behavior as well as situation- or story-driven learning and teaching situations. On the basis
of an adaptive mechanism in ELEKTRA’s game engine, the learner’s experience will be
personalized and also guided through learning situations by following pedagogical and didactic
rules and by adapting to the learners’ prior and current knowledge, continuously constructing new
knowledge. In this way, learners will be able to face their individual “garden of knowledge”
(Figure 3) which can be explored extensively. Moreover, teachers will be supported in including
this immersive 3D world as part of their classroom activities by closely relating the learning
objectives of ELEKTRA to national curricula and by designing learning scenarios (e.g.,
experiments or simulations) that will be directly available from outside the game.
Figure 3. ELEKTRA’s garden of knowledge: A metaphor for an immersive
3D environment that enables the learner to explore learning content.
ELEKTRA’s 3D world will be the visual representation of the learning environment. It will support
the learner in the visualization of the structure of learning content as part of a highly interactive
virtual landscape. An appropriate metaphor is a “garden of knowledge” that can be explored and
discovered by a learner, which offers new perspectives, and which allows a continuous construction
of new knowledge with an ongoing storyline of the game. This virtual landscape of knowledge is
the direct visual representation of an underlying consistent network of ontologies and concept maps
that represent the learning topic, its context and the several learning objects that belong to the topic.
It is also the underlying structure for the progression of the game-play as well as for the
background story. Furthermore the visualization by interactive 3D-graphics could render different
views of learning objects in real-time layering different perspectives from various subjects in the
same spatial environment. This potentially creates the impression of multidimensional realities,
which may change according to learning methods and didactical strategies. The concepts for
navigation and user interface will support this concept of multi modal perspectives on the same
learning content covering a holistic view. The learner model and its connection to the knowledge
representation will also contribute to the game play experience by the use of monitoring and
assessment of the learner within the environment. This will enable a situated and activity-driven
learning experience in ELEKTRA’s virtual learning environment that is able to react to the
learner’s behavior, cognitive state, and learning progress.
Current digital educational games mainly consist of two parts that separates immersive gaming
experiences from learning experiences: (a) a game world (often 2 D, sometimes 3 D) with several
tasks for the learner and (b) a learning part (2D and similar to traditional non gaming multi media
learning products), where the learner mainly has to read text or watch a film to receive information
and to gain knowledge. These games use traditional methods to support the knowledge transfer and
just imitate traditional learning material and methods such as books and films. They continue the
linear content of traditional media display formats in an interactive environment. Thus, the learner
often has to jump from the learning part to the gaming part which causes loss of the immersion and
the embedding within the context. Summarizing,
Many existing educational games are, due to high costs of state-of-the-art game
development, technologically poor. Learners can not immerse into a consistent learning
world and get discouraged due to poor quality standards and concepts for visualization,
interaction, and storytelling.
The ELEKTRA project will not be able to reduce production costs for such games,
however, it will encourage companies to invest in high-quality games and will demonstrate
that such investments are justified.
Many existing educational games do not rely on national school curricula or, at best, focus
on a very limited part of them.
ELEKTRA will focus on different national curricula and demonstrate that such learning
content can be “hidden” in an immersive game.
Most existing educational games are not based on sound pedagogical and didactical models.
At best, these games support one learning method.
ELEKTRA will incorporate a sound pedagogical model based on 8LEM [51] and the
knowledge taxonomy of Bloom and its revisions [12][48].
Many educational games focus on isolated facts, skills, or abilities. Moreover, they
generally provide a given linear sequence of presenting learning contents.
ELEKTRA will provide an adaptive approach to knowledge and skills based on Knowledge
Space Theory [26][27] and the Competence-Performance Approach [46][47] enabling
individual personalized learning paths. ELEKTRA’s learning game offers an edifice of
ideas for a complete learning topic that is based on a domain ontology of the corresponding
knowledge and learning matter and embedded in a coherent background story.
Many educational games do not provide an in-depth assessment of prior knowledge or of
knowledge and skills acquired during the game play.
ELEKTRA will provide adaptive assessment algorithms to offer a diagnostic approach that
enables adaptivity and, consequently, a balance between challenge and ability.
These aims are of course highly ambitious and optimistic but the interdisciplinary approach to
research and development let us believe, that ELEKTRA can make a significant contribution to the
future of digital game-based learning.
2.2. ELEKTRA’s interdisciplinary approach
To accomplish the optimistic aims of ELEKTRA, an interdisciplinary approach (Figure 4) is
required to address all related challenges of designing an educational game; from a psychological
perspective, from pedagogical perspective, from content and goal perspectives, from the
perspective of game design and programming, and from the perspective of commercial
exploitation. Moreover, emerging research questions must be addressed to progress to a future,
more effective and desirable game-based learning.
2.2.1. Instructional design and didactic strategies
Current models describing the scope of human learning and teaching methods are crucial for the
development of meaningful and engaging game environments and immersive and enthralling
storylines. A body research in the fields of instructional theories and personalization theories put
emphasis on this point (e.g., [54]). From the work of several authors in the field of learning
psychology and pedagogy, a renewed interest for the variety of teaching and learning methods has
emerged. On the basis of research in the domain of educational psychology (e.g., [61]), which
showed that the deployment of multiple channels reinforces learning, a balance between learning
modes can be assumed to maximize learning performance. The diversity of learning experiences by
which the learner is encouraged to learn thus emerges as a criterion of educational quality.
Figure 4. Sketch of ELEKTRA’s methodological approach.
ELEKTRA’s pedagogical basis will be the Eight Learning Events Model (8LEM), which is
a pedagogical approach emphasizing that learning events are based on eight basic components.
According to [51], on the basis of these eight components any number of training strategies can be
deduced or, the other way round, any didactic strategy can be traced on these basic components. An
advantage of the 8LEM approach is that, unlike the terms method and strategy, learning events can
refer to intentional learning as well as incidental learning. The eight learning events are:
Imitation / Modeling: Describes incidental or intentional learning through observation and
subsequent imitation. The role of a tutor or teacher is being a (role) model.
Reception / Transmission: Describes learning be receiving information or advice. The
tutor’s role is transmitting information.
Exercising / Guidance: Describes a “proceduralizing” and automation of skills. The tutor’s
role is giving learners guidance and corrections.
Exploration / Documentation: Describes learning by an investigation of information with a
certain degree of freedom. The tutor’s role is to provide guidance, sources, or access.
Experimentation / Reactivity: Describes learning through manipulations of environments
and observations of effects. The tutor’s role is to provide an environment which allows
experimentation and manipulation of objects, i.e. providing reactivity.
Creation / Confrontation: Describes learning by creating new content or objects (e.g., texts
or music). Creation also includes a reincorporation of known content. The tutor’s role is to
foster creation process or confront learners with tasks of creation.
Self-reflection / Co-reflection: Describes learning by reflecting the own knowledge and
skills and the own learning processes. The tutor’s role is to give learners guidance and help
in reflection processes.
Debate / Animation: Describes learning by social interactions such as debates, arguments,
and exchange of ideas. The tutor’s role is “animating” and inciting debates and discussions.
The advantages of this model are that it is observable meaning that its components can be identified
quantitatively and qualitatively. Moreover, the model is not deterministic; in specific situations
more than one learning event can be present. For example, watching TV might include imitation of
seen activities combined with perceiving information. From the tutor’s perspective watching TV
might include modeling and transmission. The 8LEM allows describing existing learning or
teaching strategies or programs. Moreover, from a prescriptive point of view, the model allows to
plan and track learning or teaching activities. On this basis, 8LEM allows to assure a diversification
of learning or teaching methods.
Diversification of methods is a well-established pedagogical principle. Diversification of
methods means to provide learners with a broader range of learning and teaching methods during
the learning progress, instead of using continuously the same strategy (e.g., transmission). One of
its underpinning principles is that variety benefits not only to current learning activities but also
trains students in learning to learn. Should this assumption be confirmed, the diversity of learning
experiences by which the learner is encouraged to learn would emerge as a criterion of educational
quality. The model substantiates a number of reasons for varying learning events: (a) Mathetical
polyvalence, meaning that it is in the learner’s interest to gain exposure to a whole range of
learning modes in order to become a more competent learner, polyvalent in exploiting the variety of
methods, resources, or constraints. This polyvalence becomes an even more urgent necessity in a
knowledge society as this experience of diversity prepares the learner to take advantage of any
future learning occasion. The learner's mathetics polyvalence corresponds to the teacher's “didactic
polyvalence”, i.e. the capacity to organize diverse quality learning experiences. Facilitating the
spring of polyvalent learners, the 8LEM provides a tool empowering educators for offering their
pupils a rich, i.e. multi-faceted, learning experience. (b) Epistemology, meaning the advantage of
covering a subject by means of varied events which does not lie purely in the fact that it trains the
learner in a variety of learning methods. It also has an impact on the content itself. Varying events
also means, over and beyond the question of methods, constructing and enriching the concept and
the conceptual network associated to it. A medical student will have a particular idea of the
stomach if he reads (reception) documents about that organ. But he will perceive a different facet if
he is invited to perform a free dissection of a stomach (exploration). His conceptual network will be
enriched further if, as an observer, he attends a stomach operation (imitation). When he himself has
practised stomach operations (drilling), his conception of the stomach will have evolved still
further. Finally, when he engages in discussion with his peers (debate), his conceptual network will
expand even further. As well as experiencing various learning methods, he will in so doing have
developed a multimodal approach to the concept in question. (c) Personalization, meaning the
delivery of learning paths tailored to the personal preferences of the learner. The mathetical
polyvalence principle brings back the idea, expressed prosaically, that it is not because a student
likes chocolate that she does not have to be served fruits. The practitioner should consider the
benefits of accommodating preferences or going against them. Both personalization and
polyvalence drive similar attention to diversification. Any learning object can therefore equally
serve a polyvalence or a personalization endeavour, both orientations requiring in all cases extra-
reflection, extra-work and extra-pedagogical creativity for materializing the educational ideal of a
panel of learning experiences offered to students.
The framework of 8LEM allows to plan learning and teaching on a very detailed basis, and
from a descriptive perspective it allows to assure a diversification of methods in order to empower
learning and to retain motivation.
Based on the question how well people understand or know a subject, Benjamin Bloom and
colleagues [12][48] identified three general types of learning:
(a) the cognitive domain which includes skills and knowledge,
(b) the affective domain which includes a development or growth in emotional areas
and attitudes, and
(c) the psychomotor domain which includes manual and physical skills.
For each domain Bloom, Krathwohl, and Masia identified categories for the depth of learning. The
cognitive domain, which is particularly interesting for ELEKTRA’s game-based learning approach,
involves knowledge and the development of intellectual skills. This includes the recall or
recognition of specific facts, procedural patterns, and concepts that serve in the development of
intellectual abilities and skills. There are six major categories, which are listed in order below,
starting from the simplest behavior to the most complex. The categories can be thought of as
degrees of difficulties [16] which establish a hierarchical order. A more simple level of knowledge
or ability must be given in order to reach a deeper one. The categories are
Knowledge: the recall of factual information,
Comprehension: understanding of the meaning, translation, interpolation, and interpretation
of instructions and problems,
Application: using of a concept in a new situation or unprompted use of an abstraction,
Analysis: separating material or concepts into sub-components so that its organizational
structure may be understood,
Synthesis: building a structure or pattern from diverse elements, joining parts to form a
whole, with emphasis on creating a new meaning or structure,
Evaluation: making of judgments about the value of ideas or materials.
Additionally, so-called action verbs are assigned to each category, describing recall methods or
abilities more detailed. For the category “knowledge” action verbs are, for example, “define”,
“describe”, or “label”; for the category “analysis” “compare”, “quantify”, or “measure”.
Anderson and Krathwohl [8] revised Bloom’s original taxonomy. These authors combined
both the cognitive process and knowledge dimensions. This new expanded taxonomy can help
instructional designers and teachers to write and revise learning objectives. The knowledge
dimension includes factual knowledge, conceptual knowledge, procedural knowledge, and meta-
cognitive knowledge, the cognitive process dimension includes the abilities remember, understand,
apply, analyze, evaluate, and create. Thus, learning and teaching activities can be related to one of
the 24 (4x6) combinations.
Using the categories and the related action verbs, Bloom’s taxonomy allows precisely
describing and planning learning objectives (e.g., a learner shall be able to apply knowledge about
algebra to solve an equation) and the design of knowledge assessments.
ELEKTRA will also build on the most recent pedagogic research about the potential of storytelling,
"the original form of teaching" [65][83]. Stories are important cognitive events of particular
pedagogical value because, according to [59], they encapsulate four of the crucial elements of
human communication in one rhetorical package: information, knowledge, context, and emotion.
"Educational media, such as lectures, books, TV programs, are all narrative in form, and for good
reason. Narrative provides a macro-structure, which creates global coherence, contributes to local
coherence and aids recall through its network of causal links and sign posting. Narrative is
fundamentally linked to cognition and so is particularly relevant to the design of multimedia for
learning" [50].
2.2.2. A cognitive learner model
Considering current practice in game-based learning from the perspective of cognitive science
reveals a major gap. On the one hand, the development of learning games is mostly driven by
technology and content. The rationale underlying their design may allude to general learning
principles, such as constructivist learning, but it usually does not explicitly build upon elaborated
and formalized learning theories. On the other hand, cognitive science does not yet provide the
comprehensive and ready-to-be-implemented learning theory, which is capable of capturing the
relevant factors and processes in game-based learning. This lack of ties to learning theory as well as
that of a coherent theoretical framework and research paradigm has already been acknowledged a
decade ago [33]. Since then, gaming technology has improved dramatically, but not much has been
done to study how these improvements might be incorporated into learning environments, and there
is still very little agreement as to the theoretical underpinnings of this endeavor [75].
A promising approach and starting point for establishing a sound cognitive learner model is
Knowledge Space Theory and the Competence Performance Approach.
Knowledge Space Theory (KST; [26][27]) provides a set-theoretic framework for organizing a
domain of knowledge and for representing the knowledge of individuals based on prerequisite
relations. A knowledge domain is represented by a finite set Q of problems. The knowledge state of
a learner is described as the subset of problems that s/he is able to master. Due to prerequisite
relations among the problems of a domain, not all subsets of problems are possible knowledge
states. If two problems a, b Q are in a prerequisite relation, we can assume from mastering
problem b a mastering of problem a. The collection of possible knowledge states corresponding to
a prerequisite relation, including the empty set and the whole set Q, is called a knowledge
structure
Κ
. In its original formalization, KST is purely behavioral focusing on the observable
performance and it does not refer to the skills / competencies that underlie that performance.
Several approaches for extending the KST have been published, which incorporate explicit
reference to the skills that are required for solving the problems of a domain [2][3][25][29][31]. On
of the most successful approaches is the Competence Performance Approach (CPA) by Klaus
Korossy [46][47]. The basic idea of CPA is to assume a basic set
Ε
of abstract cognitive skills that
are relevant for mastering the problems of a domain. The competence state of an individual is the
collection of all available skills, which is not directly observable but can be uncovered on the basis
of the observable performance on the problems representing the domain. As in KST, prerequisite
relations are described on the set of competencies establishing a competence structure C, which
contains all possible competence states. Utilizing skill and problem functions, families of subsets of
skills can be mapped to problems, which can be mastered with the given skills and vice versa. By
the assignment of skills to the problems of a domain, also a performance structure or problem
structure on the set of problems is induced.
Based on -in detail- specified learning contents and learning goals, the skills must be
extracted and defined. This might be lead (a) by the logical structure of learning objects, (b)
theoretically, e.g. lead by existing psychological, pedagogical, or didactic theories, (c) empirically,
e.g. as the result of empirical investigations of knowledge states of learners at different ages and
levels of knowledge, or (d) by querying experts, i.e. referring to the knowledge of experts in a
given domain such as teachers.
Once the skills required in a certain domain are defined, in a second step, a prerequisite
relation in the sense of KST has to be established among the skills. This means, it has to be
determined which of the skills are necessarily a prerequisite for acquiring another skill. Again, this
might be lead (a) by the logical structure of skills, (b) theoretically, e.g. lead by existing
psychological, pedagogical, or didactic theories, (c) empirically, e.g. as the result of empirical
investigations of knowledge states of learners at different ages and levels of knowledge, or (d) by
querying experts, i.e. referring to the knowledge of experts in a given domain such as teachers.
An example is shown in the upward drawing (or Hasse graph) of Figure 5. Table 1 equivalently
displays the same prerequisite relation as binary matrix. This matrix can be seen as a computable or
computer-understandable representation of the prerequisite relation and thus of the domain’s
Figure 5. Upward drawing illustrating a prerequisite relations among a set of skills {a1, a2, a3, b, c, d1, d2, e, f, g, h, I,
j}. Please note that the lower of two connected skills indicates a prerequisite for the upper one. Thus, as an example,
skill a2 is a necessary prerequisite for skill c and, thus, also for h, i, and j.
Table 1. Prerequisite relation displayed as binary matrix.
a1 a2 a3 b c d
1
d
2 e f g h i j
a1 1 0 0 1 0 0 0 0 0 0 1 1 1
a2 0 1 0 1 1 0 0 0 0 0 1 1 1
a3 0 0 1 0 1 0 0 0 0 0 1 1 1
b 0 0 0 1 0 0 0 0 0 0 1 1 1
c 0 0 0 0 1 0 0 0 0 0 1 1 1
d
1 0 0 0 0 0 1 0 1 1 1 1 1 1
d
2 0 0 0 0 0 0 1 1 1 1 1 1 1
e 0 0 0 0 0 0 0 1 1 1 1 1 1
f 0 0 0 0 0 0 0 0 1 0 1 1 1
g 0 0 0 0 0 0 0 0 0 1 1 1 1
h 0 0 0 0 0 0 0 0 0 0 1 1 1
i 0 0 0 0 0 0 0 0 0 0 0 1 0
j 0 0 0 0 0 0 0 0 0 0 0 0 1
Note. The cell value 1 denotes that the competency in a row is a prerequisite for the
competency in the related column. Vice versa, a cell value 0 denotes that the
competency in a row is not a prerequisite for a competency in the related column.
For example, competency b in row 4 is a prerequisite for competency h in column
11.
structure. If the prerequisite relation is established, we can compute the related competence
structure. The competence structure denotes all competence states, which are possible and
meaningful with respect to the prerequisite relation. If we assume that skills a2 and a3 are
prerequisites for skill c, competence states, which include skill c but not all of its prerequisites, in
this example a2 and a3, are not possible or meaningful; at least from a deterministic perspective.
This procedure allows us to reduce the number of possible competence states. Without
prerequisite relation, 13 skills establish a competence structure, which includes 8192 competence
states, i.e. the power set of all skills (213). The proposed prerequisite relation establishes a
competence structure, which includes only 108 possible competence states. The resulting
competence structure is displayed in Figure 6.
The competence structure defines possible and meaningful competence states and it allows
to model possible and meaningful learning paths. A major advantage is that CPA allows multiple,
individual, not necessarily linear learning paths. This offers a sound model of learning content
which establishes the very basis for game design as well as learning design. On the side of learning
design, CPA allows to continuously develop skills and competencies in a structured manner. On the
side of game design, branched storylines for interactive, personalized storytelling can be realized,
characterized by multiple paths to proceed through the game play and game environment (Figure
5). The competence structure (at least in this example) also establishes certain bottlenecks, each
learner / gamer has to pass. In the terms of game play, such competence states might be realized,
for instance, as starting points of new game levels. Thus, the nature of competence structures as a
formal representation of human skills or competencies in a given domain is quite similar to the
storytelling and action design of typical computer games. Thus, CPA offers a method to
meaningfully join learning and gaming design.
Figure 6. Competence structure established by the prerequisite relation (shown in
Figure 5) among a set of skills. Each of arrows in the magnified image denote a valid
learning step, consequently, learning progress is modeled by valid learning paths.
Besides the cognitive components of competence development, for the aims of ELEKTRA
restrictions by the curricula and pedagogical theories must be considered. Pedagogical theories
might restrict the prerequisite relation in a way that it might be pedagogically important to teach
specific skills prior to others. These restrictions provoke an altered prerequisite relation (displayed
in Figure 7, left panel) and consequently an altered competence structure (displayed in Figure 7,
right panel). Alteration, however, can only be implemented, as far as they do not violate the
assumptions of the initial prerequisite relation. For instance, an alteration cannot lead to a
constellation within which skill c is taught prior to skill a2. In this example, the number of possible
competence states is reduced to 21 out of 8192.
So far, we focused on latent unobservable skills; these must be mapped to observable performance.
CPA therefore provides representation and interpretation functions or in the terminology of [29]
skill and problem functions.
The problem function assigns the problems (e.g. test items) of each learning situation to
each competence state, within which they can be mastered. Vice versa, the problem function
assigns the competence states to the problems of each learning situation, within which they can be
mastered. These functions strongly depend on the game’s storytelling and the realization of
learning situations and its assessments. These functions do not require a one-to-one mapping,
hence, in the game scenario, there might be learning situations that include more than one skill.
The skill and problem functions can be seen as an interface between the competence
structure and the game design. A major advantage of these functions is that a change in the game
design (i.e., the design of learning or assessment activities) does not require to change the
underlying competence structure but only to edit the problem and skill functions.
To give a simple example, ELEKTRA’s scenario might include four learning units (A1 to
A4) including related learning situations and problems (e.g. assessment tasks or test items. Possibly
resulting problem and skill Functions are shown in Table 2.
Figure 7. Altered prerequisite relation (left panel) and the resulting competence structure (right panel).
Table 2. Problem and skill functions.
Problem Function
competence state Problem
{a1,a2,a3,b,c} A1
{a1,a2,a3,b,c,d1,d2,e,f,g} A1, A2
{a1,a2,a3,b,c,d1,d2,e,f,g,h,i,j} A1, A2, A3, A4
Skill Function
Problem competence state
A1 {a1,a2,a3,b,c}
A2 {a1,a2,a3,b,c,d1,d2,e,f,g}
A3 {a1,a2,a3,b,c,d1,d2,e,f,g,h,i,j}
A4 {a1,a2,a3,b,c,d1,d2,e,f,g,h,i,j}
Note. Please note that for the problem function only these competence states are listed,
within which a further problem can be accomplished. For the skill function only these
competence states are listed which are the states that include the smallest number possible
to accomplish a learning situation.
2.2.3. Game design
ELEKTRA’s 3D world will be the visual representation of the learning environment. It will support
the learner in the visualization of the knowledge space of a learning topic as part of a highly
interactive virtual landscape. For the ELEKTRA demonstrator a single-user adventure game will be
realized covering an immersive storyline. This game demonstrator, as commercial games, will
include
Storytelling situations that are realized as cut-scenes or in game storytelling explaining
the main goal of the game, as well as tasks and challenges alongside the course of the
game, the background story and the progress of the story,
tutorials to make learners familiar with game concepts and to convey game literacy
gaming situations which primarily aim for impelling the storyline and retaining
motivation and interest,
learning situations which primarily focus on providing educational content and
impartation of knowledge, and
assessment situations within which the current knowledge of a learner will be measured.
The educational contents of the demonstrator will cover the domain of optics, and as part of this
domain, particularly the topic “eclipse”. Information about eclipses will be packed into an
immersive and naturalistic 3D environment (Figure 8) and, more importantly, into a meaningful
and exciting story. Also assessments will be an integral part of the game. As an example, learning
success will be measured by observing the learner’s ability to solve certain riddles and tasks.
Learning and assessment situations are equivalently to the different stations in the campaign of a
typical adventure game and can represent both challenge (in form of problems that have to be
solved) and knowledge (that is necessary to solve the problems). The target group of the game
demonstrator will be student at the age of 14 to 15 years.
From a technical perspective, the ELEKTRA demonstrator will be based on the Nebula2
game engine (www.radonlabs.de/nebula.html) which includes a rendering engine, a game logic
engine, a physics engine, and a scripting engine. Furthermore, interfaces between game engine and
Figure 8. Example sketches of ELEKTRA’s storyboard.
adaptive and learning engines will be established. The latter are responsible for implementing the
rules and mechanisms of CPA to the game.
Computer games are quite complex pieces of software including a vast amount of menus,
functionalities, objects, maps, and so on. Many commercial and educational games lack of usable
interfaces, interaction methods, and game functionality, failing in providing pleasant and relaxed
gaming experiences. For some reasons usability engineering has not gained the extent of popularity
yet it has gained in software and web development. Gamasutra, for example, found a number of
serious usability problems in a prototypical study (http://www.gamasutra.com/features/20050623/
laitinen_01.shtml, retrieved August 24, 2006). Usability, however, is crucially important for games
[61][62]: (a) Interactive experiences are the focal point of games and users are very sensitive to
obstructions in this interaction. Because playing generally is purely voluntary, usability problems
are a serious risk for a game’s success. (b) The market of computer games is extremely
competitive; if one game fails in providing fluent and pleasant interactions, e.g. handling the
inventory of an adventure game, gamers can instantly choose another, equivalent game from the
rich offers. Thus, usability engineering is obviously more important for the game genre than for
other software categories; there are for example not too many operating systems to choose from.
When aiming for a “voluntary learning” with a game, usability and user-experience are even
more important factors. Consequently, ELEKTRA will rely on well established principles and
guidelines for usability engineering (see [38][40]) and will apply usability engineering during the
development process and in the framework of comprehensive evaluations of the game
demonstrator. Because ELEKTRA aims to be used also in classrooms or related to classroom
activities, usability engineering will also address requirements of learners with special needs (cf.
[39]).
2.3. ELEKTRA’s architecture
The very basis of the ELEKTRA methodology as well as the game demonstrator is to establish
learning objectives for the game. For the ELEKRTA demonstrator we selected the domain of
“eclipse”. This topic primarily covers physics education at the age of 14+. The learning objectives
are extracted from the national curricula of Belgium, France, and Germany; ELEKTRA thereby
covers the union set of these three curricula. In order to realize a game intelligence, as described
above, this static information have to be stored. Furthermore, dynamic information about the
learner, its preferences and knowledge must be stored and continuously updated.
2.3.1. The ontology
The “database” for relevant information used by the game demonstrator will be based on an
ontology. An ontology in computer science is a data model to represent entities of a domain and
relations among them. An ontology includes concepts or classes which are generic terms, e.g.
learning objects or test items, and it includes instances or individuals related to a set of concepts,
e.g. a specific test item. The entities of an ontology can be described with attributes. Each attribute
has a name, a certain data type, and one or more values, e.g. attribute ”language“ and value
"English". Attributes, moreover, serve to establish relations among entities. For example, a relation
might be "A is part of B". The prerequisite relation introduced in this article can be modelled in this
way. Thus, a skill (on the latent level) or a learning object / test item (on the performance level)
may have an attribute “is prerequisite of“. Ontologies establish a formalized and computable
representation of a domain and the related entities. Generally, this is accomplished with XML
metadata. A typical standard is OWL (ontology web language) which is used in ELEKTRA.
The preliminary structure of ELEKTRA’s ontology is shown in Figure 9. Its major concept is the
“learner”, emphasizing the learner-centric approach of ELEKTRA. The domain of knowledge is
Figure 9. The preliminary structure of the ontology used as “database” for static and dynamic information.
modeled in form of propositions. Equal to concept maps, propositions are established by to
concepts, an object and a subject which are connected to each other by any kind of relation. As an
example, a proposition might be [Sun – is a – star]. On the basis of a complete set of propositions
of the domain, skills can be deduced and refined in combination with depth information (this refers
to the taxonomy of Bloom [12]and related action verbs). Thus, not only detailed skills can be
deduced but also learning objectives can be defined in detail, e.g. “to define a star”.
As demonstrated in Figure 5, within the ontology prerequisite relation among the skills can be
established and combined with pedagogical implications (e.g., defined by the learning modality).
This might refer to the 8LEM but also to didactical strategies such as problem-based learning or
factual learning approaches. As described above, so far, skills are on a latent, unobservable level.
According to CPA, the ontology allows us to relate a set of learning situations of the game and also
a set of assessment situations to skills and specific sets of skills. Thus, we can model problem and
skill functions (cf. Table 2). Finally, the ontology allows us to model and store further learner
information, e.g., motivational states or learning styles, in order to provide a suitable database for
making the game adaptively and adequately react to certain learner requirements.
As mentioned, in contrast to traditional CPA approaches, the ontology-based approach
allows to model pedagogical implications, for example implications by 8LEM or didactic
strategies. The lines in Figures 5 and 6 indicate learning by accomplishing specific learning
situations. These “transitions” are based on purely cognitive modeling of the domain. However, this
cognitive theory does not make any advice of how the learning activity must be designed or
presented in the game. The selection of an appropriate learning activity for the transitions
(represented by the upwards directed lines in the diagram) has to be grounded on pedagogical
principles. Figure 10 demonstrates the performance structure derived from the sample interpretation
and representation functions of Table 2. One or more learning situations may lead from one
performance state (meaning a learner / gamer is able to master a specific task or assessment;
indicated by symbols A1 to A4) to another, superior one. This is indicated in Figure 10 by eight
lines connecting the performance states. Bold lines indicate that not necessarily all of the eight
learning events must be realized as a learning situation in the game. Additionally, the ontology
allows us to adaptively refer to certain learning paths according to the needs of learners or
instructional designers and educators.
Besides selecting appropriate learning or teaching methods (i.e., learning situations)
pedagogical implications might also influence the selection of appropriate learning paths. The
magnified area in Figure 6, for example, displays eight possible learning paths; for pedagogical
implications in interaction with individual preferences only some of these learning paths might be
appropriate. The ontology allows referring to a data basis to adaptively react to such implications
on a larger scale.
Figure 10. Performance structure established by the problem and skill functions of Table 2.
The progress from one performance state to another is realized by one or more learning
situations which are determined by 8LEM.
2.3.2. Adaptivity
Games generally react on user actions; for example, when a gamer fails in a certain task s/he might
lose energy or power. An educational game must react on user actions related to learning in the
same manner. This, however, is not a trivial task.
Systems that attempt to adapt to a learner’s competence or performance state are well-
established in traditional forms of eLearning. Generally, these systems are used to tailor learners’
views of learning objects to their personal requirements. Such technologies are often incorporated
to guide through a large body of learning objects assisting learners in their comprehension of that
material. For example, an adaptive system may only provide learning objects which are suitable for
the learner. Too difficult and also too easy learning objects might not be displayed in order to avoid
visual and cognitive load and to suggest an appropriate learning path through the learning content.
Existing adaptive eLearning systems such as ALEKS (www.aleks.com) or ELM-ART [82]
demonstrated benefits for learners in terms of navigating through contents [14] and in terms of
classroom and platform independence [13]. Generally, these adaptive approaches attempt to
compete the one-fits-all approach of traditional eLearning accounting for certain requirements and
preferences of a learner. Primarily, such approaches provide adaptive navigation and adaptive
presentation of contents [4][5][20]. Adaptive navigation, as already mentioned, refers to guidance
through learning objects by, for example, a customized hyperlink structure or format. The degree of
freedom granted within a system is determined by a specific underlying learner model. Adaptive
presentation refers to a customized presentation of learning objects. On the one hand this might
concern the visual or auditory design; on the other hand this might concern the amount or grade of
details of presented learning contents.
In ELEKTRA, adaptivity regarding the learner’s knowledge will be realized by providing
appropriate learning situations for a learner’s current competence state. This means, a learner will
be provided with learning situations and learning paths within the game environment that are not
too easy or too difficult. This assures that meaningful and efficient learning is possible and, on the
other hand, this assures an appropriate balance between challenge and skill in order to retain
motivation and engagement. Thus, the game requires adequate assessment situations.
In the scope of the game, there are three types of assessment: (a) An initial assessment of a
learner’s competence state before starting the game, (b) a continuous assessment of learning
progress, and (c) a final assessment measuring the overall learning success, for game levels and for
the entire game.
For the initial assessment we will provide adaptive assessment algorithms, which are able to
reduce the number of test items to determine a learners / gamers competence state. Generally, the
exact assessment of a competence state requires as much test items as competencies. Referring to
the competence structure, CPA allows concluding from mastering or failing in a certain test item,
which test items can be skipped. In the game, the initial assessment might be realized as a tutorial
level, which is common in many games.
Crucially important for the game process is a continuous assessment of learning success,
because learning success (meaning the acquisition of competencies) determines the way in which
learning situations can be realized for the learner. According to CPA, no learning objects shall be
presented to the learner, which require skills this person does not already have. In the game this
might be realized by certain tasks, which must be accomplished to make headway in the game's
story.
The final assessment might be covered by the same tasks; since the CPA-based storytelling
will be designed that new levels or the end of the game can only be reached when all necessary
competencies are learned. Additionally, we have to consider forgetting, especially when factual
learning is involved or the prerequisite relation is altered by pedagogical and storytelling
constraints, which might lead to relations which are not based on logical cognitive prerequisites.
Thus, at the end of levels or the entire game, it might be useful to realize comprehensive
assessments. As for the initial assessment such procedures might be based on adaptive assessment
algorithms.
To realize the assessment, the game engine will be connected to a so-called adaptive engine
and a learning engine, which implement the rules of CPA and make suggestions for the progress in
the game. Thus, adaptive and learning engines can be viewed as interface between the game itself
and the information stored in the ontology.
Because ELETRA will not only be adaptive regarding knowledge but also regarding
motivational states, learning styles, or didactical strategies, the same approach can be used to
provide adaptivity in this regard (cf. [43]). Thus, assessment will involve meta-cognitive judgment
on the answer the learner provides. We will provide then reflection tools based on the meta-
cognitive information they give with their judgments, to improve their cognitive and meta-
cognitive performances. But we will also link the feedback to the learning strategies they have used
through the game in resolving the problems and challenges. Progress in learning will be more
precise for it and will be more detailed in terms of granularity. For instance, an answer can still be
wrong in a following test but if the answer is associated with a lower confidence degree we have
quantitative data proving the progress. The same is true for a correct answer with a low confidence
degree in which progress still can be made by increasing the confidence degree in that correct
answer. As
De Finetti [22] stated: ”Partial information exists. To detect it is necessary and feasible (p. 109). It
is only subjective probability that can give an objective meaning to every response and scoring
method (p. 111)”.
3. Research questions and evaluation
As mentioned, currently there are a number of open research questions. Van Eck [79] pointed out
that research must clarify why educational games are effective and give practical guidelines how
game-based learning can be implemented successfully in or to maximize learning potential.
Moreover, he emphasizes the importance to investigate not only how educational games work, but
also how different types of games work and how game taxonomies relate to learning taxonomies.
It’s clear that not all types of games work equally well for each learning objective. For example,
simple card games might be adequate for pattern recognition or the matching of concepts,
adventure games, on the other hand, likely be adequate for promoting hypothesis testing or problem
solving.
Moreover, ELEKTRA’s achievement of the general objectives - the production of the
demonstrator and methodology - will be measured in empirical evaluations carried out with
learners as they are actively engaged in using the ELEKTRA demonstrator in classrooms, single
user tests and laboratory studies, ensuring an empirical evaluation of the effectiveness of this
demonstrator. The formal cognitive modeling requires deriving parameters that explicitly
characterize the educational efficiency and effectiveness not only at a global level (e.g. for
evaluating the employed teaching method), but also at a highly specific level (charactering, for
example, single learning objects, or learning activities).
These aims for research and evaluation / validation are realized as an interdisciplinary
process, combining research of cognitive science, neuroscience, pedagogy, and game research.
From a cognitive perspective, major research questions concern the validation of the domain
structure or the implications of instructional design on learning and performance. Neuroscience will
contribute sophisticated methods such as event-related fMRI (functional magnetic resonance
imaging), which allows identifying brain activity related to the recognition and encoding of events,
such as words, faces, or objects and the retrieval of brain representations of these events from
memory. Research paradigms enabling a reliable neuroimaging of “insight” during learning are still
lacking. This is why the ELEKTRA-evaluation scenarios will concentrate on facts, not on
“insight”. From the perspective of pedagogy, important research questions concern the impact of
method diversification on learning or meta-cognition. Finally from the perspective of game
research and game design, important questions are how to merge adequate game design and
didactical design (cf. [79]) or what are the mechanisms to achieve immersive visuals and
storytelling by retain pedagogical demands.
4. Conclusion
The ELEKTRA project has highly ambitious and visionary aims to make a significant contribution
to the evolution of digital game-based learning. In consideration of the vast amount of gaming in all
cultures, the enormous intrinsic motivation of games, and the fact the gaming is one of the most
natural and effective forms of learning, this is a estimable and important aim, especially for an
information and knowledge society which assets are primarily based on knowledge, competence,
and know-how.
The problems with most current game-based learning reported in the literature are manifold.
The costs of developing high quality games are very high, games which tend to be educational and
engaging often tend to privilege engaging game elements over pedagogical demands, or vice versa.
Generally educational games fail to be related to school curricula or to provide appropriate
assessment. Cynically, Seymour Papert [64] termed the problems in merging state-of-the-art game
design with educational demands “Shavian reversal”, meaning that the offspring inherits the worst
of both parents (educational design and game design).
Figure 11. A proposal for the design process for educational games.
The ELEKTRA project aims for addressing these problems and to establish a methodology
for state-of-the-art educational game design and to provide the related technology. Figure 11
summarizes ELEKTRA’s methodological approach to design educational games. To align an
educational game with curricula and contents, in a first step existing national curricula for a given
learning topic must be identified and analyzed. On this basis learning objects can be defined in
detailed referring to pedagogical models and taxonomies (e.g., 8LEM or Bloom’s taxonomy).
Moreover, learning topic and curriculum determine the game genre and, subsequently, the
background story which covers the educational aim. As Van Eck [79] pointed out, not all types of
games are suitable for each educational aim and it is an important question to clarify which game
types are suitable for which learning topic. In a next step an ontology of the knowledge domain
must be established, including skills in a sufficiently fine granularity and prerequisite relations
among these skills. Third, learning and assessment situations must be developed according to the
learning objectives and the game genre. This task might be the most difficult one since it requires
creativity and fantasy. Finally, story telling and game design must meld these components into a
immersive and motivating game.
Relying on the body of research on digital game-based learning, computer games have the
potential to foster learning and to offer new and rich perspectives and experiences. Despite the
disadvantages and problems of existing educational games, we believe that games hold out the
prospect of melding gaming for learning and gaming for fun to a much broader and successful
extent. We are optimistic that ELEKTRA can make a significant contribution to the enthralling
future of game-based learning.
7. Acknowledgements
The research and development project introduced in this work (ELEKTRA) is granted as a specific
targeted research or innovation project (STREP) by the European Commission under the sixth
framework programme in the IST research priority, contract number 027986.
The authors wish to thank all consortium members that so devotedly contributed to
ELEKTRA and to this work.
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Purpose The purpose of this paper is to present principles for personalization of both learning content and gameplay in serious games for learning, which are based on a combined model of the student that comprises user, learner and player-related aspects of the student’s profile. Each of the considered user, learner and player sub-models has a static and dynamic group of characteristics. These characteristics assist general approaches for learning mazes game personalization applied to embedded mini-games (designed as information units, learning objects and educational tasks) so that to be adjustable and to enable learners to acquire knowledge more effectively. Design/methodology/approach A student modelling approach was applied to design the personalization of learning content in the educational maze game and each of them contained mini-games. To evaluate the student’s preferences about the types of mini-games and ways of their personalization depending on individual and group student characteristics, the authors conducted an online survey. Findings This study presents examples of personalization of four types of mini-games available in maze halls, namely, question, searching, arranging and action games. Next, the research discusses findings from an online survey aiming at the evaluation of the preferred types of mini-games and the way of their personalization. There are analysed results concerning the impact of the student model characteristics on the preferred ways of personalization in educational maze games, together with criteria for personalization of educational resources according to student’s level of knowledge, age, goals and learning style. Research limitations/implications A significant limitation of the research is the relatively small number of survey participants and the lack of studying the impact of learning and playing styles over game personalization. Another limitation of the study is the inclusion of only some of the mini-games within the demonstration maze, which respondents play before answering the survey questions. Originality/value This paper presents original research on the personalization of educational maze game based on a model of the student profile that comprises both static and dynamic properties reflecting user, learner and player-related aspects of the student character, together with results obtained from an online survey.
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In this work, the authors analyze the video game genres’ features and investigate potential mappings to specific didactic approaches in the context of Physics education. To guide the analysis, the authors briefly review the main didactic approaches for Physics and identify qualities that can be projected into game features. Based on the characteristics of the didactic approaches each video game genre’s potential for narration and simulation and affordances for reflection and assessment are evaluated, providing examples of specific games that adhere to those requirements and ways they can be utilized in educational contexts. The paper concludes by discussing the implications on serious game design and integration of games for Physics education in school environments and suggests topics for future research.
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The power of seduction generated by video games in the new generations makes its use in education promising, which helps to achieve a highly motivated group of students and obtain a more efficient learning. This is how the Serious Games and the Gamification arise. The first ones are interactive software specially designed to favor the acquisition of knowledge and skills or behavior changes, in an environment similar to videogames. The second one just applies the elements and mechanisms that make videogames captivating, although used in non-game contexts. The idea behind this consists in offering students playful/fun motivational experiences and transforming the learning process into a much more attractive one. The experiences already conducted with both proposals provide results that invite us to continue moving forward on that path.
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Educational games offer different ways for representing complex themes. However, creating a game that precisely addresses the subject being taught and effectively provides students’ learning and engagement is a complicated task. With this in mind, we researched and created an educational game for improving students’ Software Process Improvement (SPI) knowledge. The aim of this game is to bring practices from the software development industry to the students. In an attempt to provide a better guidance, this work describes the issues and challenges associated with the creation and validation process of a Software Engineering educational game. We adopted an incremental and iterative approach, where each step involves different knowledge, allowing us to point out a set of important aspects that should be taken into account during the development. These aspects can guide new developers and instructors in the design and selection of educational games.
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Procedures which are to test a subject’s knowledge concerning a specific domain obviously require (in addition to other prerequisites) a set of problems.
Article
This exploratory study investigated the influence of two recreational computer games on children’s subsequent performance on computer-based instructional tasks. Children were assigned to three groups: two were invited to play their respective recreational computer games, and the third acted as a control group. All three groups then worked on a common set of educational tasks from environmental education software. The three groups’ performances on a set of educational tasks were compared using quantitative analysis for speed and correct solutions, and then qualitatively for the cognitive manoeuvres engaged in to accomplish the tasks. The findings suggest that playing recreational computer games may influence children’s performance on subsequent computer-based educational tasks. However, the extent of this influence depended on how closely the recreational computer game types matched the design of the tasks in the educational software. The cognitive manoeuvres used by game players also depended on the types of games played during the learning phase. Linear cause-and-effect games tended to encourage means–end analysis strategy, whereas adventure games encouraged inferential and proactive thinking. Though the findings of this study are encouraging, further studies need to be undertaken to replicate the results.
Article
his investigation examined individual learning differences and adaptive learning in interactive World Wide Web (Web or WWW) learning environments from a new perspective. This per-spective differs from traditional highly cognitive approaches be-cause it highlights the dominant influence of emotions and inten-tions and their impact on learning. The study introduced learner-difference profiles called learning orientations and the System for Intentional Learning and Performance Assessment (SILPA), an interactive Web learning environment, to investigate how individ-uals manage learning in environments designed to support learn-ing orientations. To accomplish this purpose, this study used learning orientation to (a) add the learner-difference dimension to the research design and analytical models (b) differentiate the learning audience, (c) guide the design of the learning environ-ment and presentation of the treatment, and (d) make interpreta-tions about the results. A secondary purpose was to test the SILPA as a promising new instructional and formative research model for Web learning. See: https://www.learntechlib.org/p/15187/ for full text
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Pourquoi, à l'école, certains élèves font-ils tout pour ne rien faire, alors que d'autres décident de s'engager à fond dans leurs études ? Pourquoi mettent-ils peu d'entrain à effectuer les activités qu'on leur propose, alors que d'autres y consacrent toute l'énergie nécessaire ? Pourquoi abandonnent-ils leurs activités à la moindre occasion, alors que d'autres persévèrent jusqu'au bout ? Toutes ces questions ont en commun la motivation en contexte scolaire. Les intervenants en milieu scolaire, particulièrement les enseignants, savent que la motivation joue un rôle de premier plan dans l'apprentissage. Or, si tous les enseignants le savent, beaucoup méconnaissent l'exploitation de cette dimension chez l'élève. Ce livre veut proposer des pistes concrètes d'action sur la motivation.
Article
Gender differences in mental rotation and computer experiences were investigated. Undergraduates, 27 men and 83 women, completed a brief self-report inventory of computer experiences and were pretested on the Vandenberg Test of Mental Rotation (VTMR). Students then participated in two 30-min sessions, spaced 1 week apart, in which they played a computer game. One half played a game that required mental rotation of geometric figures (‘Blockout’), the other half played a card game that did not involve mental rotation (‘Solitaire’). After the second computer game session, students retook the VTMR. A gender difference favoring men over women was obtained on the pretest VTMR. On the posttest VTMR, women in each group and men who played Blockout outperformed men who played Solitaire. Differences in selfreported computer use and efficacy were associated with differences on the VTMR. Success on Blockout was correlated with success on the VTMR. Computer experiences, including game playing, are a factor in VTMR performance differences among undergraduates.
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This paper explores the conceptual representations of spatial relationships in manipulable (or table-top“) space. Three cognitive tests demonstrate that subjects drawn from two Tamil subpopulations tended to mentally encode relationships among objects in a way which correlates with their different habitual uses of linguistic spatial reference.
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The manifest popularity of computer games for many children has led to speculation about the meaning and effects of their use. However, research has yet to provide an adequate picture of the social characteristics of computer game players. This article, based on a large-scale study of Flemish 10- to 11-year-olds, is intended to sketch a profile of the computer game heavy user. The results indicate that heavy use of computer games is associated with negative rather than positive outcomes in terms of academic achievement, self-esteem and sociability. Finally, a LISREL model is tested which assesses the contribution which school-related factors, in interaction with gender and socioeconomic status, make in accounting for the varying amounts of time children spend playing computer games.
Article
Thirteen male secondary school pupils (CHIPs) displaying relatively high levels of interest in, and knowledge about, computers were selected from a sample of 429 respondents to a questionnaire about computers. Their responses to the Bem Sex Role Inventory (BSRI), the Group Embedded Figures Test (GEFT) and the Youth Telic Dominance Scale (YTDS) were compared with the responses of 14 matched respondents (Matches) selected because of their relative lack of interest in computers. In order to provide normative data 34 controls (Controls) also completed the three scales. The CHIP and Match samples were also interviewed. Results indicated that the CHIPs obtained significantly lower ‘masculine’ scores on the BSRI, scored higher on the GEFT and obtained significantly higher arousal avoidance scores on the YTDS than the other samples. Analysis of the interview transcripts, however, did not support the commonly held view that heavy computer users are less sociable than their peers.