GAME-BASED LEARNING

Conceptual Methodology for Creating Educational Games

Stephanie B. Linek

Institute of Psychology, University of Graz, Universitätsplatz 2, 8010 Graz, Austria

Daniel Schwarz

Takomat GmbH, Neptunplatz 6b, 50823 Cologne, Germany

Matthias Bopp

Center for Advanced Imaging, Brain Research Institut, University of Bremen, Hochschulring 18, 28359 Bremen, Germany

Dietrich Albert

Institute of Psychology, University of Graz, Universitätsplatz 2, 8010 Graz, Austria

Keywords: Game-based learning, Methodology, Evaluation, Competence-based Knowledge Space Theory, Media

psychology.

Abstract: Game-based learning builds upon the idea of using the enjoyment and the motivational potential of video

games in the educational context. Thus, the design of educational games has to address optimizing

enjoyment as well as optimizing learning. Within the EC-project ELEKTRA a methodology about the

conceptual design of digital learning games was developed. Thereby state-of-the-art psycho-pedagogical

approaches (like the Competence-based Knowledge Space Theory) were combined with insights of media-

psychology (e.g., on parasocial interaction) as well as with best-practice game design. This science-based

interdisciplinary approach was enriched by enclosed empirical research to answer open questions on

educational game-design. Additionally, several evaluation-cycles were implemented to achieve further

improvements. The psycho-pedagogical core of the methodology can be summarized by the ELEKTRA’s

4Ms: Macroadaptivity, Microadaptivity, Metacognition and Motivation. The conceptual framework of the

developed methodology is structured in eight phases which have several interconnections and feedback-

cycles that enable a close interdisciplinary collaboration between game design, pedagogy, cognitive science

and media psychology.

1 INTRODUCTION

Game-based learning is a relatively new research

area and so far there exist no concrete systematic

recommendations for the conceptualization of an

integrated design of educational games.

In the following, a newly developed conceptual

framework for the creation of educational

(adventure-)games will be outlined and illustrated by

several concrete examples and empirical

(evaluation) studies. The proposed methodology was

developed and successfully used in the EC-project

ELEKTRA (Enhanced Learning Experience and

Knowledge Transfer). The described process can

serve as a model for other contexts of game based-

learning as well as the creation of serious games.

1.1 Game-based Learning

Game-based learning is a kind of edutainment that

rests upon the idea of using the motivational and

immersive potential of conventional video games in

the educational context. Even though there are

several publications on games, game-play (Salen &

135

Linek S., Schwarz D., Bopp M. and Albert D.

GAME-BASED LEARNING - Conceptual Methodology for Creating Educational Games.

DOI: 10.5220/0001824901350142

In Proceedings of the Fifth International Conference on Web Information Systems and Technologies (WEBIST 2009), page

ISBN: 978-989-8111-81-4

Zimmermann, 2004) and game-based learning

(Prensky, 2005), the divers contributions are often

rather unconnected and an overall framework for the

creation of educational games is still missing.

The main problem in this context is the seldom

collaboration between psycho-pedagogical scientist

and industrial game designers. For an appropriate

serious game design, both, the creativity of game

designers as well as the expertise of psycho-

pedagogical scientists are necessary.

A first step in this direction was made within the

EC-project ELEKTRA which will be described in

the next subchapter.

1.2 EC-project ELEKTRA

ELEKTRA (Enhanced Learning Experience and

Knowledge Transfer) was an EC-project under FP6

on game-based learning. The aim of the

interdisciplinary research project was twofold: On

the one hand it aimed at the development of a state-

of-the-art educational adventure-game to make

learning as exciting as leading-edge computer

games. For this practical aim the so-called

ELEKTRA-demonstrator was developed which

comprises the first chapter of an educational

adventure-game on the learning domain

physics/optics. On the other hand a general

methodology about the conceptual design and

production of digital learning games should be

established. This second aim was accomplished by

the ELEKTRA methodology which will be

described in this article.

The core idea of producing effective and

motivating digital game-based e-learning

experiences for young children relies on an

interdisciplinary approach which combines state-of-

the-art research in cognitive science, pedagogical

theory and neuroscience with best industrial practice

in computer game design.

The developed methodology builds not only a

framework for structuring and supporting the

interdisciplinary cooperation, but also inherent

several interrelated phases and evaluation-cycles that

enable continuous improvements and enhancements

of the educational game design.

1.3 The ELEKTRA Methodology:

Overview

On a general level, the ELEKTRA methodology

does not re-invent the wheel but shares a lot of

elements with usual instructional design models that

many readers might be familiar with (e.g., Brown &

Green, 2006). In particular the proposed

methodology can be seen as an adaption of the Dick

and Carey System Approach Model (Dick, Carey &

Carey, 2005) – revised for the purpose of making a

state-of-the-art digital learning game.

The base of the developed methodology can be

summarized by the ELEKTRA’s 4Ms:

Macroadaptivity, Microadaptivity, Metacognition,

and Motivation. Within the ELEKTRA-project we

identified these 4Ms as the pivotal elements of an

(exciting) educational game (independent of the

concrete learning content and storyline/genre of the

game). In order to manage the workflow within the

interdisciplinary collaboration a framework with

eight phases was developed:

 Phase 1: Identify instructional goals

 Phase 2: Instructional analysis

 Phase 3: Analyse learners and context of

learning

 Phase 4: Write performance objectives and

overall structure of the game

 Phase 5: Learning game design

 Phase 6: Production and development

 Phase 7: Evaluation of learning

 Phase 8: Revise instruction

Even though these phases are numbered from one

to eight, they do not follow a linear order but have

several interconnections and feedback cycles. Figure

1 illustrates the workflow within the eight phases of

the model.

The ELEKTRA’s 4Ms are mainly addressed in

phase 5 which can be suggested as the core of the

methodology: the learning game design. But also the

other phases relate to the 4Ms in an implicit way:

The phases before feed in the learning game design,

the succeeding phases rely on the learning game

design and its implementation and improvements,

respectively.

In the following, first the ELEKTRA’s 4Ms will

be characterized. Second, the eight phases will be

described; thereby the focus lies on the

psychological contribution within this framework.

Several practical and empirical examples from the

ELEKTRA-project will be given. Finally, a short

resume will be provided.

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136

RES 422

Identify

instructional

goals

Instructional

analysis

- Create Knowledge

structure

Analyse

learners and

context of

learning

- User requirements

-User preferences

- User‘s entry skills

Writeperformance

Objectives+overall

structureofthegame

Evaluation of

learning

- User validation

- Scientific

validation design

-Testing

(functional +

pedagogical)

Revise instruction

- Analyse user validation

- Recommendations

- Revise and update:

Instructional goals, user requirements, instructional analysis, Learning Game Design, Production and Development via RAD-approach

Learning Game Design

Didactic

Design

-LeS

(Macro-

adaptivity,

Metacognition,

Microadaptivity

(pedagogical rules,

adaptive elements))

Design story-

based game

world

- Game world and

mechanics

-GpS

- Background story

- Characters

-StS

- Assessment +

Validation

instruments

(e.g. Logfiles)

- Microadaptivity

(Update learner

model, Assess-

ment, adaptivity)

What to learn How to learn How to make

CONCEPTION

How to

evaluate

DESIGN

PRODUCTION

DEVELOPMENT

VALIDATION

CONTENT LEARNING TECHNOLOGY

Production and

Development

- Technology

development

- Content production

- Game release

Design In-Game

Assessment

Overview

on 8

phases

model

- Game chapters

- Categorize learning

Objectives

- Triple consistency

- Learning methods

- Performance structure

RES 422

Identify

instructional

goals

Instructional

analysis

- Create Knowledge

structure

Analyse

learners and

context of

learning

- User requirements

-User preferences

- User‘s entry skills

Writeperformance

Objectives+overall

structureofthegame

Evaluation of

learning

- User validation

- Scientific

validation design

-Testing

(functional +

pedagogical)

Revise instruction

- Analyse user validation

- Recommendations

- Revise and update:

Instructional goals, user requirements, instructional analysis, Learning Game Design, Production and Development via RAD-approach

Learning Game Design

Didactic

Design

-LeS

(Macro-

adaptivity,

Metacognition,

Microadaptivity

(pedagogical rules,

adaptive elements))

Design story-

based game

world

- Game world and

mechanics

-GpS

- Background story

- Characters

-StS

Design story-

based game

world

- Game world and

mechanics

-GpS

- Background story

- Characters

-StS

- Assessment +

Validation

instruments

(e.g. Logfiles)

- Microadaptivity

(Update learner

model, Assess-

ment, adaptivity)

What to learn How to learn How to make

CONCEPTION

How to

evaluate

DESIGN

PRODUCTION

DEVELOPMENT

VALIDATION

CONTENT LEARNING TECHNOLOGY

Production and

Development

- Technology

development

- Content production

- Game release

Production and

Development

- Technology

development

- Content production

- Game release

M1M1

M4M4

Design In-Game

Assessment

M2M2

M3M3

Overview

on 8

phases

model

- Game chapters

- Categorize learning

Objectives

- Triple consistency

- Learning methods

- Performance structure

Figure 1: Overview on eight phases of the model.

2 ELEKTRA’S 4MS

The ELEKTRA’s 4Ms include the pivotal features

of a successful educational game. The headwords

Macroadaptivity, Microadaptivity, Metacognition,

and Motivation are only rough catch phrases for

various elaborated concepts, models and findings.

Within the ELEKTRA project the main

psychological contributions regard to

microadaptivity and motivation. The work on

macroadaptivity and metacognition was mainly part

of the pedagogical partners.

2.1 M1 - Macroadaptivity

Macroadaptivity deals with the adaptive pedagogical

sequencing of alternative learning situations for one

learning objective. Thereby macroadaptivity refers

to the instructional design and management of the

available learning situation. It addresses the

adaptivity between different learning situations and

refers also to a diversification of learning based on

Bloom’s taxonomy (1956).

The macroadaptive process leads to the creation

of a learning path which represents a specific

combination of divers learning situations.

2.2 M2 - Microadaptivity

Microadaptivity regards to adaptive interventions

within a learning situation. It involves a detailed

understanding of the learner’s skills and a set of

pedagogical rules that determine the interventions

given to the learner. Within ELEKTRA the idea

behind the concept of microadaptivity (Albert,

Hockemeyer, Kickmeier-Rust, Pierce, & Conlan,

2007) is to develop a system that provides hints

adapted on the user’s (current) knowledge and

competence state. Whereas macroadaptivity refers to

traditional techniques of adaptation such as adaptive

presentation and adaptive navigation on the level of

different learning situations microadaptivity deals

with the adaptivity within a single learning situation.

The basis of the microadaptive skill assessment

and the non-invasive interventions is a formal model

for interpreting a learner’s (problem solving)

behavior. To realize the non-invasive skill-

assessment and the adaptive interventions,

ELEKTRA relies on the formal framework of the

Competence-based Knowledge Space Theory

(CbKST; Albert & Lukas, 1999; Doignon &

Falmagne, 1999; Korossy, 1997). Originating from

GAME-BASED LEARNING - Conceptual Methodology for Creating Educational Games

137

conventional adaptive and personalized tutoring, this

set-theoretic framework allows assumptions about

the structure of skills of a domain of knowledge and

to link the latent skills with the observable behavior.

Microadaptivity in this context means that the

intervention/hint was selected on the basis of

knowledge assessment via CbKST. The chosen hint

provides either the necessary information to solve

the problem (to learn a missing skill) or the affective

support (e.g., motivating or activating feedback)

fitting the current progress state of the learner

assessed by his action history.

LearningSituation

Position_Category

Objects

Competence

CompetenceState

Learner

CompetenceSet

Related_Objects*

in_les*

SkillSets_Required*

position_category*

poscat_related_object*

poscat_skills_missing*

poscat_skills_required*

Includes_Skills*

Skills_Taught*

Has_Skill*

Has_Prerequisite*

incl_skills*

has_skillstate*

Figure 2: Microadaptivity – integrated model.

Within the ELKTRA-demonstrator the

microadaptive interventions were presented by a

non-player character (NPC) named Galileo in order

to merge microadaptivity with the storyline and the

overall game play.

Figure 3: Using the non-player character named Galileo

for providing microadaptive interventions.

2.3 M3 - Metacognition

Regarding Flavell (1976, p. 232) “Metacognition

refers to ones knowledge concerning one‘s own

cognitive processes or anything related to them, e.g.,

the learning-relevant properties of information or

data”. Even though there exists slightly different

interpretations of this original definition,

metacognition is agreed to involve knowledge about

one’s own knowledge as well as knowledge about

one’s own cognitive processes. The ability of the

ELEKTRA-demonstrator to foster metacognitive

development was considered as a major challenge

and an important differentiator compared to

traditional educational games.

The integration of a reflective pause in the game-

based learning process seems at first sight

contradicted to storytelling and the flow of game

play. Within ELEKTRA the resolution to this

dilemma is based on two pillars: First, the

implementation of certitude degrees, i.e., while

performing a task, the learner has to indicate the

prudence and confidence he has in his performance.

Second, a firm support of this kind of metacognition

by the storytelling, i.e., the prudence and confidence

estimation were made in a close parasocial dialog

with the NPC Galileo.

The metacognitive reflections therefore are

tightly bound to the gaming process. Thereby the

ELEKTRA-demonstrator contributes to develop not

only the ability to perform, but also to understand

the conditions of success, and thus, having cognitive

and sometimes metacognitive goals in addition to

the pure performing goal.

2.4 M4 - Motivation

The fourth M named Motivation comprises several

motivational concepts and related approaches used

for enjoyment and learning. Motivation in this sense

is only a keyword for different aspects of the

storyline, the challenges and skills (flow-

experience), the intrinsic motivation of the gamer,

the parasocial interaction and empathy with the

NPCs as well as the identification with the avatar.

In general, motivation is a phrase used to refer to

the reason(s) for engaging in certain activities. In the

context of learning games, the creation of motivation

to engage in and perform learning activities is a core

element of good game design and can be suggested

as the major advantage of educational games

compared to other ways of e-learning.

There are many aspects of games which are

suggested to contribute to the gamers motivation

(Vorderer & Bryant, 2006), e.g., competition,

parasocial interaction with the NPCs fantasy,

escapism, suspense or curiosity as well as the

balance between challenges and skills (enabled by

different game-levels) which in turn fosters the so-

called flow-experience (Csikszentmihalyi, 1990).

Within ELEKTRA we mainly focussed on the

storyline and the game characters as motivational

tools for learning. This includes the creation of a

story that adds “sense” to specific learning activities,

i.e., the learning activities are an integrative part of

the story itself. Thereby the story confronts the

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player with certain game-challenges/problems (e.g.,

riddles) that he can only solve when he first learns

certain skills and the story makes it worth to do so.

Figure 4: Riddle within the ELEKTRA-demonstrator:

Solution requires knowledge on optics.

A typical example would be that the learning

activities influence the fate of the avatar or the good

and bad NPCs. The crucial thing is to merge

learning activities and storyline in a playful way.

The usage of the storyline (including game-

characters) as a motivational tool comprises several

subtasks: designing a setting, a general plot,

interesting good and bad characters with which the

players can have an immersive parasocial interaction

and an avatar with which the players can easily

identify.

3 DESCRIPTION OF THE EIGHT

PHASES

In the following the eight phases will be described.

Like mentioned above it is important to note, that

these phases don’t follow a simple linear order but

rather comprise several interconnections and

feedback cycles (see also figure 1).

The psycho-pedagogical contributions within

ELEKTRA regarded mainly to the phases of

instructional analysis, the analysis of the learners

and the context of learning, the learning game design

and the evaluation phase. For these phases concrete

practical and empirical examples will be given to

illustrate the important part of cognitive science and

media psychology in the conception of educational

games.

3.1 Phase 1: Identify Instructional

Goals

In this early stage, pedagogy clearly prevails the

overall game design by setting some fundamental

pedagogical and didactical decisions with respect to

the chosen learning goals, the basic areas of learning

content and the general pedagogical approach. The

context of the game has to be outlined as well:

Should the learning game be deployed in a class-

room situation at school or should it be played at

home as a spare-time activity? This decision is

another important cornerstone for the general

conditions of the whole design of the learning game.

After the definition of learning goals, topic,

target group, learning content, pedagogical approach

and the context, the general framework of the game

is settled. This pedagogical framework not only

constitutes the learning experience in the game, but

also has got a fundamental impact on the overall

concept of the game design. The choice of the game

genre is the first crucial design decision which is

directly dependent on the learning objectives. If you

like to create for example a strategic simulation

game, you would perhaps choose different types of

learning goals than for a racing game.

3.2 Phase 2: Instructional Analysis

In phase 2 the learning objectives and the related

learning content are transferred into a formal

knowledge structure which is called knowledge

space. The theoretical background and

mathematical-formal framework is delivered by the

already mentioned CbKST. In this context, the main

advantage of the CbKST is the clear distinction

between observable behaviour and the underlying

skills and their interrelationships. Thereby the

prerequisite relations between skills as well as

between behaviours enable the adaptation to the

actual available skills of the learner as well as the

adaptation to the ongoing learning progress.

In the established knowledge space all of the

learning objectives are represented as an ontology of

skills. Thereby the accordingly skills are structured

as a map that allows analyzing the developing

knowledge state of the learner and thus a learner

model. In addition, it allows adapting the game

environment to the individual learning needs of the

player. This can take place on different levels, e.g.,

on the level of macroadaptivity or on the level of

microadaptivity.

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139

3.3 Phase 3: Analyse Learners and

Context of Learning

Phase 3 contributes to the detailed analysis of the

learners and the context of learning. Thereby the

characteristics of the learner group concerning entry

skills, learning problems, preferences and attitudes

are determined. In a learning game, these areas refer

to the learning process as well as to the game play

(Linek, 2007). Thereby the twofold role of the target

user has to be taken into account: he is both, a

learner and a player.

Entry skills for the learner could be known

difficulties in the chosen learning topic. Additional,

entry skills for the player could be the state of his

game literacy.

The learner analysis serves as input for a variety

of game decisions: For example the NPC design, the

visual style of the game, and the provision of

specific learning methods. It is also used to

determine the initial state of the learner model.

These decisions could be partly made by help of

existing literature and research findings. However,

with respect to the concrete game design partly

additional empirical studies might be necessary.

For example within the ELEKTRA-project a

focussed multimedia study on the NPC-design

(regarding his friendliness, the naturalism of the

graphics and the role of color) was conducted.

Figure 5: Experimental design of the so-called NPC-study.

The results of this so-called NPC-study indicate

a clear preference for a colored, naturalistic NPC-

design. For the NPC’s friendliness the pupils favor a

NPC that was similar to their own, indicating

similarity-attraction (Linek, Schwarz, Hirschberg,

Kickmeier-Rust, & Albert, 2007).

3.4 Phase 4: Write Performance

Objective and Overall Structure of

the Game

On the basis of phases 1 to 3, performance

objectives are laid out and, closely linked to this, the

overall pedagogical structure of the game is written.

This basic scenario is a kind of working paper which

will go through various changes throughout the

continuing revising process of the creation for the

game.

In particular the overall pedagogical structure

should include a general description of the story of

the game (including the setting, the characters, and

the plot), the game-chapters as well as various

situations of the game that build up the chapters.

They are described in a rough way which mainly

includes their main functionality within the game

and their possible sequences which can include

adaptive branches.

3.5 Phase 5: Learning Game Design

Phase 5 is the very core of the ELEKTRA

methodology and the accordingly design of a

learning game. It is the central work phase where the

successful integration of learning and gaming takes

place and everything comes together. The main task

in this phase is to develop detailed descriptions of

each situation in the game: Learning situations

(LeS), gameplay situations (GpS), and storytelling

situations (StS). Every situation must be described in

terms of stage, possible actions, and events that

happen in the environment in reaction to the player’s

activities. The output is a “Game Design Document”

which gives programmers (development) and artists

(content production) precise instructions for the

development and production of the educational

game.

The challenge of this design process is to design

those three types of situations in such a manner that

they constitute pedagogical valid learning activities

that are embedded in a meaningful and exiting

learning game experience for the player.

In an ideal learning game experience the three

essential situation types work together as ingredients

of a new experience which would arrange a superior

game situation from games, learning, and

storytelling. This ideal is not always achievable but

at least the gameplay situations, learning situations

and storytelling situations should motivate, amplify

and legitimate each other by embedding them into a

meaningful context.

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Game authoring

Pedagogy

Cognitive Science

Game Design

Design + Arts

Ideal user experience

in a digital learning game would

comprise a mutual pervasion of the

three identified situational

components (Learning situation,

Story situation, Gameplay situation)

in ONE LEARNING GAME-Situation

(LGS):

Learning

Situation

Story

Situation

Game play

Situation

Digital learning game

as a whole

In Game

Assess-

ment

NEURO-

SCIENCE

USER-

TESTING

New Learning experience

Validation of Learning

HOME-

WORK

SCHOOL

Transfer of Learning

Learning

Situation

Figure 6: The ideal learning game situation.

The conceptual tools for the design of the

situations and their sequencing are based on the

already described ELEKTRA’s 4Ms. Thereby

Macroadaptivity, Microadaptivity, and

Metacognition are mainly concepts of the

instructional strategy of the learning situations and

the appending in-game assessments, while

Motivation is rather the objective of the story-based

game world.

3.6 Phase 6: Production and

Development

There are two main work areas in the production and

development phase: On the one hand programmers

develop the various technologies required for the

game, on the other hand, artists and producers create

all the media assets that are necessary to build the

game world. Roughly spoken, one can say, that the

development team works on the logic of the game

while the production team creates the data for it.

The necessary input for the development team

and the content production team are the pedagogical

scenarios written in phase 4 and the Game Design

Document of phase 5. During phase 6 there is a

vivid exchange between the programmers of the

development team and the artists and producers of

the content production team.

The outcome of this phase is a published release

version of the game that can be tested, played and

evaluated.

3.7 Phase 7: Evaluation of Learning

There are two different forms of evaluation: the

formative evaluation and the summative evaluation

of the game.

The formative evaluation is called testing and is

closely connected with the development and

production work in phase 6. Ideally, the formative

evaluation should take place in (monthly) timeboxes

when a new testable version of the game-prototype

with the latest implementations and improvements is

delivered (as output of phase 6). This iterative

timebox releases will undergo each time a functional

and psycho-pedagogical testing. The formative

evaluation can concentrate on single game-elements

like background-music or game characters or might

deal with the implementation of a new approach like

microadaptivity in ELEKTRA (Linek, Marte, &

Albert, 2008). The evaluation results of this testing

will directly feed back into early phases.

Thereby, the report on technical testing describes

functional bugs that manifest themselves in mistakes

of the game system. The programmers then have to

correct or change the according software

components. The report of the psycho-pedagogical

testing relates to gaming and learning experiences of

the target end user. The results of the psycho-

pedagogical evaluation forces sometimes even to go

back to the design phase (5).

The summative evaluation can be described as a

general evaluation of the developed game and the

whole process. It takes place when the iterative

technical testing leads to a stable running and

psycho-pedagogical meaningful version of the game.

In order to analyse the learning behavior and success

of the pupils in the game and their evaluation of the

gaming experience as a whole, a science-based

methodology is applied, using standardized

questionnaires as well as logfile-information. In this

context not only control variables and pre-

questionnaires are considered, but also long-term

effects of the learning-game experience should be

assessed (e.g., to assess the long-term knowledge

gain).

3.8 Phase 8: Revise Instructions

Subsequent to the game testing and the empirical

summative evaluation, the next essential step is to

interpret and exploit the evaluation results for

providing recommendations for improvements and

enhancements of the learning game as a whole.

These recommendations have to be feed in all

preceding phases, affecting all previous tasks and

activities and hence, might resulting in a revision

and update of the instructional goals (phase 1),

instructional analysis (phase 2), user requirements

and preferences (phase 3), learning game design

(phase 5) as well as production and development

(phase 6).

Moreover, also the implementation of the

evaluation itself might befall revision, e.g., in case

GAME-BASED LEARNING - Conceptual Methodology for Creating Educational Games

141

of an emerging need for improving the assessment

instruments / questionnaires. This in turn requires a

close collaboration between scientific research and

evaluation. Accordingly, research partners are

responsible for selecting scientific sound evaluation

instruments as well as for proposing an adequate

methodology and data-analysis.

4 CONCLUSIONS

The proposed methodology delivered a general

conceptual framework for the creation of a broad

spectrum of educational games. The applicability

and validity of the methodology was firstly proven

within the ELEKTRA-project. The ELEKTRA-

demonstrator was evaluated empirically and proved

its effectiveness for enjoyment as well as for

learning. Besides this first positive evidence of the

effectiveness of the proposed methodology, also the

newly developed micropadaptivity-formalism was

successfully tested in several empirical pilot-studies

(Linek, Marte, & Albert, 2008).

The proposed ELEKTRA methodology can be

suggested as a first framework for designing a broad

spectrum of educational games. The framework is

flexible and open for new technical developments

and possibilities and bears the potential to integrate

new scientific psycho-pedagogical concepts.

Accordingly, the described methodology can be

suggested as an open framework that can be adapted

to the concrete needs and aims of game-designers,

scientists and the target end users.

ACKNOWLEDGEMENTS

This paper is part of the ELEKTRA-project funded

by the Sixth Framework Programme of the European

Commission’s IST-Programme (contract no.

027986). The author is solely responsible for the

content of this paper. It does not represent the

opinion of the European Community.

Thanks to the ELEKTRA-Team for the inspiring

interdisciplinary work!!!

REFERENCES

Albert, D., Hockemeyer, C., Kickmeier-Rust, M. D.,

Pierce, N., & Conlan, O. (2007). Microadaptivity

within complex learning situations – a personalized

approach based on competence structures and

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