Games
Engineering
Prof. Dr. Sebastian von Mammen
Leiter der Arbeitsgruppe Games Engineering Lehrstuhl für Mensch-Computer-Interaktion Fakultät für Mathematik & Informatik
Serious Games
als Lernbeschleuniger - worauf kommt es an?
Games
Engineering
Lehre/Betreuung
- Games Engineering, B.Sc.
- Computer Science, M.Sc
- Human-Computer Systems, B.Sc.
- Human-Computer Interaction, M.Sc.
Derzeitige Mitarbeiter/innen
- Annika Fabricius, M.A.
finanziert durch die Universitätsbibliothek
- Mounsif Chetitah, M.Sc.
- Sooraj K. Babu, M.Sc.
- Sarah Hofmann, B.Sc.
- Johannes Büttner, B.Sc.
- Samuel Truman, B.Sc.
- Damian Kutzias, M.Sc.
with Fraunhofer IAO
- Andreas Müller, M.Sc.
finanziert durch den Lehrstuhl Physikalische Chemie,
- Simon Seibt, M.Sc.
an der TH Nürnberg
- Helge Olberding, M.Sc.
an der FHWS
+ HiWis
Echtzeitfähige Interaktive
Systeme Interaktive Simulation
Künstliches Leben
Künstliche Intelligenz
Wissenschaftliche Schwerpunkte
Games
Engineering
Games Engineering, B.Sc.
- Game Labs 1, 2, 3 (je 15 bis 20 ECTS)
- Games Engineering Wahlfächer (5 bis 10 ECTS)
- Interaktive Künstliche Intelligenz (5 ECTS)
- Asset Development - Modellierung & Animation (5 ECTS)
- Seminar (5 ECTS)
- Praktikum (15 ECTS)
- Bachelorarbeit (15 ECTS)
Informatik, M.Sc. *
- Game Research Labs in den Bereichen:
Theorie/Systeme/Gestaltung/Anwendung (je 10 ECTS) - Seminar (5 ECTS)
- Wissenschaftliches Praktikum (10 ECTS)
- Masterarbeit (30 ECTS)
https://games.uni-wuerzburg.de
Games
Engineering
global state
local state
topology
attributes values
0 10 20 30 40
1 1.5 2 2.5 3
generation f
max_c2
max_c1
avg_c2
avg_c1
Künstliches Leben
Selbst-
organisierende Systeme
Adaptive Systeme
A Model and Pipeline for Interactive Simulation of Morphological Biology
Andreas Knote and Sebastian von Mammen
Julius-Maximilians-Universit¨at, W¨urzburg
{andreas.knote, sebastian.von.mammen}@uni-wuerzburg.de
Abstract
In this work, we present our current efforts towards a com- prehensive, human-in-the-loop modelling framework for the study of complex morphogenetic systems. The state of our physical cell model providing localized surface-based inter- actions and built on top of a real-time capable particle-based physics engine is summarized. We further outline our long- term concept towards an integrated pipeline for automated model generation and refinement based on empirical data and human-in-the-loop simulations. With it, we strive to seamlessly integrate with a biologist’s workflow, for exam- ple through appropriate import and annotation tools for em- pirically obtained data, and intuitive and accessible tools and languages for behaviour description. To integrate the differ- ent software components into a real-time interactive system, we use UnrealEngine4, a state-of-the-art game engine.
Introduction
We aim to provide an interactive, immersive, and real-time framework for the modelling and simulation of morpho- genetic systems, see Figure 1. At its core, our concept en- visions a cell-centred simulation approach, where biologi- cal cells are represented as autonomous spatial agents with explicit physical shape and local, surface-based interactions embedded in a fluid dynamic simulation for substance dif- fusion. This core model needs to be augmented with an ac-
Informs EmpowersV
isual ScriptingPhysical Cell Model Empirical Data Pipeline
Figure 1: The basic components of our proposed concept.
An interactive, particle-based physical cell model (bottom) can be programmed using accessible tools such as visual scripting languages (middle) and integrated directly with empirically obtained data (top). For the latter, a comprehen- sive import and annotation pipeline is provided. Based on
Evolutionäre Algorithmen
Büttner et al., 2020
von Mammen, 2016 Rudolph et al., 2016
von Mammen & Jacob, 2008
Knote & von Mammen, 2017
Däschinger et al., 2017
Games
Engineering
Künstliche Intelligenz
below occupied (=0)
Comb*
(=1)
Comb1 below
Comb*
p = 1.0 t = 1
(=1)
Comb2 place
Agenten- basierte
Modellierung Procedural
Content Generation
Knote & von Mammen, 2019
von Mammen et al., 2010
Shirazi et al., 2014 Ziegler & von Mammen, 2020
von Mammen & Jacob, 2008 [22]
Yasin & von Mammen, 2021
Kutzias & von Mammen, 2021
von Mammen, Wagner et al., 2017
von Mammen & Tar on, 2017
Games
Engineering
Echtzeitfähige Interaktive
Systeme
Graphics Physics Streaming
Scene
Camera Blood Vessel
Blood Cell Server
Transform
Transform
Transform
Mesh
Mesh
User Interaction
(Blueprint) Tick
Push Flex Data To GPU Update Cohesion Springs
Update Rigidbodies
Update Solver Pull Flex Data From GPU
Synchronize Synchronize Cell Particle
Data
GameThread (CPU) FleX (GPU)
ACellSimulation::Tick
Video Player
Control Twitch Extension
Web Browser Video Player
Control Twitch Extension
Twitch
Rendering Game Mechanics Video Game
(Unity) Viewer/Player #1
WebSocket Server Web Browser
Viewer/Player #n
Visuelles
Programmieren
Software Engineering
von Mammen, Schellmoser et al., 2016
Ciolkowski et al., 2017
Truman et al., 2018
Wodarczyk & von Mammen, 2020
von Mammen et al., 2010
Knote & von Mammen, 2018
Games
Engineering
Interaktive Simulation
BAC in ! Effects
0.3 - Slight decrease in vision performance, of concentration and judgement
- Impaired reflexes 0.5 - Speed is miscalculated
- Vision decreases by about 15%
- Reduced hearing
0.8 - Vision decreases by about 25%
- Tunnel vision
- Reaction time prolonged by 30 to 50%
- Balance disorders
1.0-2.0 - Further deterioration of vision performance - Disorientation and problems with spatial vision - Significantly disrupted responsiveness
- Loss of the ability to accept criticism 2.0-3.0 - Strong balance and concentration problems
- Memory gaps and confusion - Vomiting and muscle relaxation at 3.0 - Unconsciousness and memory loss
- Weak breathing and loss of reflexes at 4.0 - Paralysis, coma and death
TABLE I: The adverse effects of alcohol on the human body.
and ultimately boring experience. To keep an element of surprise, diamonds and beer cans are randomly distributed along the track. In order to ensure that the driver is not forced into any obstacles, i.e. to ensure that skilled driving provides for a smooth escape from any beer cans on the track, we defined spawning areas for the two types of icons (which could overlap) and allowed the spawned icons to shift and rotate within predefined ranges. This technique is similar to calculating potential solutions to inverse kinematics based on time and space constraints, see for instance [10]. Cars driving the opposite direction also add to a varied gaming experience.
They implement a simple behaviour, following a given path as closely as possible.
The realistic demonstration of the effects of alcohol is an important part of the model. It is based on data from the German federal centre for health education [11]. Table I sum- marises the physiological impairment. We focused on visual effects. With an increase in BAC, our visual perception blurs, gets shaky, loses colour and the edges get darker. Impacts on our sense of hearing is another important facet. Sounds appear muffled and less clear. Consequently, the feedback from and the knowledge about our environment is heavily diminished.
To infer concrete numbers, we assumed a 20 year old, 1,75m tall male driver, weighing 75kg. We further assumed a beer can to contain half a litre of (Bavarian) beer with 5% alcohol
0.0! 0
.6!
1.3! 2
.0!
2.7! 3
.3!
Fig. 6: Succession of increasingly low performing vision with increasing blood alcohol concentration.
IV. RESULTS & FUTURE WORK
Different from its precursors, Drink & Drive offers a fun and informative gaming experience about alcohol misuse in road traffic. It implements a scientifically backed model of perceptional and reaction impairments similar to numerous serious, non-game simulators but it also introduces gamifi- cation elements to engage young players. The impairment model is based on scientifically determined facts, but the corresponding visualisation can only be an approximation. In general, Drink & Drive is not set out to achieve great realism but rather to integrate the serious contents that are valuable and the game elements that trigger intrinsic motivation to engage the players. In particular, we tried to realise some of Koster’s work, speaking to the users’ competence, to establish relationships with the game contents but also social links providing a competitive environment, and to ease the user into playing, maintaining his autonomy as much as possible [12].
In a competition on interactive simulations, we presented
Logistik
Medizin/
Gesundheit Serious Games
Schikarski et al., 2015
von Mammen & Schmidt, 2019 von Mammen, Knote et al., 2016
von Mammen, Lehner et al., 2015
Meisch et al., 2017
Heinrich et al., 2019
von Mammen, Müller et al., 2019
Knote, Fischer et al., 2019
Jac ob et al., 2012
von Mammen & Schmidt, 2019 von Mammen, Knote et al., 2016
BAC in ! Effects
0.3 - Slight decrease in vision performance, of concentration and judgement
- Impaired reflexes
0.5 - Speed is miscalculated
- Vision decreases by about 15%
- Reduced hearing
0.8 - Vision decreases by about 25%
- Tunnel vision
- Reaction time prolonged by 30 to 50%
- Balance disorders
1.0-2.0 - Further deterioration of vision performance - Disorientation and problems with spatial vision - Significantly disrupted responsiveness
- Loss of the ability to accept criticism
2.0-3.0 - Strong balance and concentration problems - Memory gaps and confusion
- Vomiting and muscle relaxation at 3.0 - Unconsciousness and memory loss
- Weak breathing and loss of reflexes at 4.0 - Paralysis, coma and death
TABLE I: The adverse effects of alcohol on the human body.
and ultimately boring experience. To keep an element of surprise, diamonds and beer cans are randomly distributed along the track. In order to ensure that the driver is not forced into any obstacles, i.e. to ensure that skilled driving provides for a smooth escape from any beer cans on the track, we defined spawning areas for the two types of icons (which could overlap) and allowed the spawned icons to shift and rotate within predefined ranges. This technique is similar to calculating potential solutions to inverse kinematics based on time and space constraints, see for instance [10]. Cars driving the opposite direction also add to a varied gaming experience.
They implement a simple behaviour, following a given path as closely as possible.
The realistic demonstration of the effects of alcohol is an important part of the model. It is based on data from the German federal centre for health education [11]. Table I sum- marises the physiological impairment. We focused on visual effects. With an increase in BAC, our visual perception blurs, gets shaky, loses colour and the edges get darker. Impacts on our sense of hearing is another important facet. Sounds appear muffled and less clear. Consequently, the feedback from and the knowledge about our environment is heavily diminished.
To infer concrete numbers, we assumed a 20 year old, 1,75m tall male driver, weighing 75kg. We further assumed a beer can to contain half a litre of (Bavarian) beer with 5% alcohol strength. This leads to one beer equaling about 0.3 ! of BAC.
The body shuts down at around 3 ! . As a result, collecting ten cans of beer results in loosing the game.
There are no impediments at the beginning of the game.
The player always starts the ride sober. Intoxication first results in a slight deterioration of clear-sightedness. Next, serious impairments start taking effect. At 0.6 ! , the vision gets darker
0 . 0 ! 0
. 6 !
1 . 3 ! 2
. 0 !
2 . 7 ! 3
. 3 !
Fig. 6: Succession of increasingly low performing vision with increasing blood alcohol concentration.
IV. R ESULTS & F UTURE W ORK
Different from its precursors, Drink & Drive offers a fun and informative gaming experience about alcohol misuse in road traffic. It implements a scientifically backed model of perceptional and reaction impairments similar to numerous serious, non-game simulators but it also introduces gamifi- cation elements to engage young players. The impairment model is based on scientifically determined facts, but the corresponding visualisation can only be an approximation. In general, Drink & Drive is not set out to achieve great realism but rather to integrate the serious contents that are valuable and the game elements that trigger intrinsic motivation to engage the players. In particular, we tried to realise some of Koster’s work, speaking to the users’ competence, to establish relationships with the game contents but also social links providing a competitive environment, and to ease the user into playing, maintaining his autonomy as much as possible [12].
In a competition on interactive simulations, we presented Drink & Drive to about 40 people, most of them students. They voted the game to be the best out of 15 projects, including in- teractive simulations about ants foraging, bee colony defence, and medical surgery. Criteria in the competition comprised the complexity of the scientific model, usability, and visual appeal.
As a next step, we will present Drink & Drive to a younger subset of the target audience (at ages 14 to 18) and inquire about their findings. We hope this study to allow for various
Games
Engineering
8
Serious Games
Schikarski et al., 2015
Figure 2: Screenshot of the game
Interface
The following controls and information widgets can be seen on the game screen:
• info bar: at the top, shows how much money you have right now, how much you will gain (or lose) this year, what year it is, and how many years are left until the next elections.
• tile settings: top left, use this dialog to change the type or use intensity of the tile(s) you have currently selected (selected tiles have a red border).
• approval bars: top right, show how happy each voter group is with you.
• turn button: bottom right, click this to advance to the next year.
• overlays: bottom left, click the leaf button to show the biodiversity overlay, or the cog button to show the productivity overlay. Use these to see which areas have high or low biodiversity and productivity (brighter colours mean higher values).
The controls are as follows:
• camera movement: Scroll to zoom in or out. WASD to pan, QE to rotate ( space rotates back to default).
• tile selection: Click on a tile to select it. shift -click to select an additional tile, click-and-drag for area selection. Double-click to select a specific habitat.
(a) Separation
(b) Cohesion
(c) Alignment
Fig. 6: The aspects of basic boid behaviour as proposed by Reynolds. Taken from [19]. Each image depicts how the force (red arrow) that affects the boid (green triangle) in relation to the others (blue triangles) is composed. Only the boids that lie within a specific radius visualised by the grey circle are considered.
Fig. 7: An in-game school of fish.
Fig. 8: The visualisation of aquatic plants. Comparison of two models and their animated states.
Summarising, an exemplary user survey could acquire the following data from the player:
• Age and gender.
• Level of knowledge concerning climate change and its effects on the environment.
• Level of knowledge concerning aquatic plants and lake ecosystems.
• Preconditions concerning video games, e.g. experience in years or average video game playing duration per unit of time.
• Enjoyment and excitement while playing the game.
• Experienced level of immersion.
• Ease of playing / learning how to play.
• Quantity and quality of knowledge acquired while play- ing the game.
• Interest in the topic (before and after playing).
Within the scope of this course, it was not possible to conduct large-scale structured play-testing with representative player surveys. However, the game has been tested in various stages of the prototype. Most testers stated that the atmosphere, no- tably the music, the water colour and the animated fish, was perceived as particularly positive. The most negative aspect was that some testers had difficulties with the installation process, which includes installing Julia and linking it in the game. As a consequence, the process has been improved by enhancing the user guidance and feedback.
IV. C ONCLUSION
To summarise, one can say that based on the motivation to teach knowledge and raise awareness of the ecosystem in lakes and the effects of climate change on this system in a pleasant context, a serious point-and-click video game has been developed. The game design decisions were made by applying state-of-the-art academical knowledge of video game development. The player is taught knowledge in an enjoyable way as is retrieved as a reward for fulfilling various in-game activities. Subsequently, the knowledge is
x y
z
A B
Games
Engineering
Serious Games
“unterhalten einen erziehungswissenschaft- lichen oder Lernaspekt und sind nicht nur für Unterhaltung bestimmt”
De Freitas & Liarokapis 2011
Definition
haben “ein Ziel jenseits der Unterhaltung”
Games
Engineering
Serious Games
• von Plato bis Piaget: Spielen heißt Lernen
• die ersten Serious Games waren
Simulationen, wie bspw. das Limonadenstand- Spiel von 1973
Kurze Geschichte
11
Games
Engineering
Game Engines
Code Module
• die ein Spiel antreiben
• unabhängig von den konkreten Inhalten
Gregory 2019
Definition
Games
Engineering
Game Engines
Google Books Ngram Viewer, 2019
Games
Engineering
Serious Games
• von Plato bis Piaget: Spielen heißt Lernen
• die ersten Serious Games waren
Simulationen, wie bspw. das Limonadenstand- Spiel von 1973
• erste Gestaltungsrichtlinien in den 80er Jahren
(bspw. Melone, 1981)
• America’s Army und Serious Games-Initiative 2002
Wilkinson 2016
Kurze Geschichte
15
Games
Engineering
Serious Games
• Lobbyismus: Werbespiele, Ecogames, News Games,…
• Lernen: Spatiotemporal, logisch, Fakten,…
über alle Disziplinen hinweg
• Training: Sensorimotor, kognitiv
• Therapie: Physiologisch, psychologisch
• “Welt Retten”: Puzzle lösen, Big Data,…
Ziele
Games
Engineering
“die Verwendung von Spielelementen in nicht-spielerischem Kontext“
Deterding et al. 2011
Gamification
Definition
Games
Engineering
Games
Engineering
https:/ /www .y outube.c om/ w atch ?v=ol_RRzJodtI
Games
Engineering
• Bewegung (room-scale und Teleportation zu vordefinierten Zielpunkten)
• Manuelle Interaktion (Hebel, Knöpfe, Münzen,
“Methoden, die von Agenten aufgerufen werden, um eine Interaktion mit dem Spielzustand zu
ermöglichen”
Sicart 2008
Spielemechaniken
Games
Engineering
Games
Engineering
• Bekannte cart-Mechaniken
• Begrenzte Rundenzeit (fordert sensorimotorische Spielleistung)
• Blutalkohol (reduziert sensorimotorische Spielleistung)
• Highscore (objektiviert und normiert die Spielleistung)
… lehren den negativen Einfluss von Alkohol auf sensorimotorische Leistungsfähigkeit.
Spielemechaniken
Games
Engineering
https:/ /www .y outube.c om/ w atch ?v=X Q9isXgUKaQ
Games
Engineering
• Bekannte First-Person-Shooter-Mechaniken
… werden direkt angewandt, um effizientes Zerlegen von Netzwerken zu trainieren.
Spielmechaniken
Games
Engineering
Lernziele Mechaniken
Netzwerkzerlegung
Zielen und Schießen, Gesundheitsstatus, Boni
Performanzverlust
wg. Blutalkohol Fahrkontrolle
Historische
Umgebung und Narrativ
Navigation und
manuelle Interaktion
Stärk er e Beziehung zw . Zielen und Mechanik en
Games
Engineering
Spiele-
entwicklung
1. Konzept
2. Pre-Produktion 3. Prototyp
4. Produktion 5. Alpha
6. Beta 7. Gold
8. Post-Produktion
Phasen
esi D
gn
ro P
ty to pe
ate
Agile Entwicklung
nce Co
alise ptu
ro P
to ty pe
P laytest
E va at lu e
ign Des
er
am G e
2. Ebene der agilen
Entwicklung
Games
Engineering
Nutzerinteraktion
Domänenmodell
Games
Engineering
Level Design
• Demographische Daten
• Wiederholbares Experiment
• Standardisierte Fragebögen
• Aufgabenspezifische Performanzmessung
• Analyse: Performanz vs. Schwierigkeitsstufe
Evaluation
Games
Engineering
Gestaltungsumgebung für Serious Games
Entwicklung eines strukturierten Szenarios mithilfe dreier
Modellierungsschritte:
- Pädagogische Zielsetzung
- Interaktions-basiertes pädagogisches Szenario
- Unterhaltungselemente
Tran et al. 2010
Werkzeuge
Games
Engineering
Werkzeuge ATTAC-L
• Domänen-spezifische Modellierungssprache
• Zwischen pädagogischer Gestaltung & narrativeer Modellierung
Van Broeckhoven et al. 2015
Games
Engineering
4.2 Network Analysis of Game Elements
Figure 2. Network diagram of game elements.
Outcome of network analysis is presented on Figure 2. It describes well the richness of the game domain but it is not easy to read due to the huge number of detail. Network analysis illustrates clearly that the central game element is challenges. This is also in accordance with the general game design framework – designing challenges is the central point of game design and development (Adams, 2009). But also story, emotions, game world and intrinsic motivators play important roles. The results of network analysis are different from frequency analysis because the importance of a game element is not purely based on how frequently this element was mentioned but also how many connections it has to other game elements.
4.3 Concept Map of Game Elements
Abilities
Competences
Absolute Difficulty
Difficulty Achievements Badges
Cognitive Needs
Performance
Actions
Gameplay
Altruism Recruiting
Support
Art
Avatar
Game Aesthetics
Game World
NPC
ArtworkAtmosphere
Immersion
Attitudes
Environmental Aspects
Psychological Needs
Autonomy
Control
Self-Determination
Self Expression Extrinsic Reward
Feedback
Group ID Reputation
Resource Acquisition
Balance
Concentration Merging
Big Boss
Competition
Challenges
CreativityEmotions
Engagement
Feeling Competent
Fun
Intrinsic Reward
Meaningfulness
Story Characters Empathy
Imagination
Cheating
Fairness
Intrinsic Motivation
Collaboration
Socialization
Communication
Community
Enjoyment
Competition mode
Interaction
Loosing Self Time Transformation
Conditions
Rules
FLOW - Autotelic experience
Followership
Meaningful Choices
Relative Difficulty
Social Needs
Creative Play
Fantasy
Culture
Curiosity
Decision Making
Ethics
Interactions
Details
Space
Levels
DimensionsDiscovering
Emotional Needs
Engrossment
Involvement
Extrinsic Motivation
Events
Experience
Perceived Difficulty
Motivation
Reward
Fight
Risk
Goals
Group Work
Identity
Merging
Importance
Interaction model
Interest
Knowledge
Feedback
Scoreboard
Location
Loyalty
Luck
Uncertainty
Making Decisions Messages
Opponent Actions Outside Factors
Physiological Needs
Player
Points
Positive Feedback
Preferences
Progress
Relatedness
Relations
Resources
Roles
Teamwork
Safe Environment
Self Identification Skills
Sound Status
Stile
Surprise
Time
Time Adjustments
Time Anomalies
Time Jumps
Time Pressure
Turns
Risk
Unknown Information
Utility
Voluntariness
Sillaots et al. 2016
31
Games
Engineering
Lerntheorien
• Behaviorismus
• Kognitivismus
• Konstruktionismus
• …
Ertmer & Newby 1993
Rahmenwerke für das Lernen
• (Revised) Bloom’s taxonomy
• Gardner’s multiple intelligences
• …
Mosely et al. 2005
Spiel-
elemente
Systematische
Gestaltung
Games
Engineering
LM-GM Framework
Lim et al. 2015
Figure 2. Learning and game mechanics used as the basis to construct the LM-GM map for a game
The LM nodes illustrated in figure 2 are a non-exhaustive list of learning mechanics that have been extracted from literature and discussions with educational theorists on 21 st century pedagogy, considering a variety of educational theories (e.g., constructivism, behaviourism, personalism), in particular those closer to game education (Keller, 1983; Gagnè, 1992; Papert and Harel, 1991; Brainerd, 1978). In the same manner, the GM nodes were obtained by reviewing articles on game mechanics and dynamics, and they represent the backbone of many game theories (Järvinen, 2008; Sicart, 2008;
Bellotti et al, 2009a; Bellotti et al, 2009b; Connolly et al., 2012). Proper combinations of these mechanics may be applied in several SG application domains, from languages to science, humanities and arts.
Application
For simplicity, the reading of the LM-GM model can be viewed as having two axes. On the horizontal axis lie the learning and game mechanics analogous to a breadth-first search. Core components run vertically down from the two root nodes (of learning mechanics and game mechanics respectively) in a manner similar to a depth-first search. Side or leaf nodes represent functional mechanics supporting the core.
From a pedagogical perspective, one would argue that how a user learns is, in essence, more important than the domain specificity of the medium through which the learning is performed. Based on Bloom’s theory (Bloom, 1956), a simplified framework/ classification (Table 2) organised in line with the digital taxonomy of Anderson and Krathwohl (2001) can be used to link commonly found game mechanics to learning mechanism. As an example, this table emphasises upon task-centred learning rather than cognitive learning. Indeed, a game can be seen as a continuous assessment of gained knowledge as the player proceeds from level to level.
So, a user of the model should identify which LM and GM are (or should be, in case of design) used in each game situation (among the ones listed in Figure 2), describe their relationships and implementation
Table 2. Classifications based on Bloom’s ORDERED Thinking Skills
By exploring the LM-GM model, the GALA network aims to address the mismatch between game mechanics and educational components at the design and development level. The model enables further questioning as to whether the games should adapt to existing pedagogical practices or whether they should be used to change practices since they form an entity which functions to educate and entertain through a single compelling experience. The impact from the SGMs investigations would draw out larger research themes on the intersections of games and pedagogy (both traditional and new). It will also pave the way for a toolset rather than a black box for designing content specific SG. It is important to note, though, that the LM-GM framework is not a formulaic means to design SGs. The purpose of the LM-GM is to support working with SGMs by functioning as a regression tool for developers and as analytic tool for those interested in studying the mechanisms joining pedagogical and game features.
Case study: LM-GM as an analysis tool
In this section, we describe a case study aimed at showing how to apply the framework in the analysis of the relationships between pedagogy and game mechanics in a state of the art SG such as Re-Mission (Kato et al., 2008).
Re-Mission is a game of the third-person-shooter (TPS) genre set within the bodies of young patients diagnosed with cancer, in which the player is tasked with aiding a virtual patient combat the disease and its effects. This game was chosen given its popularity and acknowledged effectiveness in the field, and because of the need to understand better whether its game mechanics at their implementation level are inherently pedagogically beneficial. Reported works (Kato et al, 2008; Tate et al, 2009, Wouters et al, 2011; Cole et al, 2012; Mader et al, 2012) on Re-Mission often do not sufficiently specify measures related to productive learning as a result of the game mechanics. Indeed, in several SGs, extraneous (i.e.
pedagogy-independent) game mechanics are often designed to enhance game play. Consequently, learning occurs only tangentially, and mainly due to the contents. However, providing contents non-
33
Games
Engineering
Lerntheorien
• Behaviorismus
• Kognitivismus
• Konstruktionismus
• …
Ertmer & Newby 1993
Rahmenwerke für das Lernen
• (Revised) Bloom’s taxonomy
• Gardner’s multiple intelligences
• …
Mosely et al. 2005
Game Taxonomien
einschl. Spielelemente und assoziierter Zielsetzungen
E.g. De Lope & Medina-Medina 2017
Systematische Gestaltung
Spiel-
elemente
Games
Engineering
“eine Menge an Konzepten und Kategorien einer Domäne, die ihre Eigenschaften und
Beziehungen zum Ausdruck bringt”
Oxford Dictionary 2020
Ontologie
Definition
Games
Engineering
“Daten über Daten” ➤ “Daten, die Web-Ressourcen beschreiben”
wobei
- “Ressoucen” beliebige Objekte sein können und
- “Aussagen” Attribute mit Ressourcen verknüpfen
Lassila et al. 1998
Resource Description
Framework (RDF)
Idee
Games
Engineering
CleanWorld
Barbosa et al. 2014
Games
Engineering
HAS_CA TEGOR
Y
HAS_OBJECTIVE
HAS_OBJECTIVE HAS_OBJECTIVE
HAS_OBJECTIVE
HAS_KNO…
HAS_INSTRUC…
HAS_INSTRU…
HAS_K…
HAS_INSTRU…
HAS_KNO…
HAS_INSTRUCTIO…
HAS_KNOWLEDG…
BELONGS_T
O_COGNITIVE_PROCESS BELONGS_T
O_COGNITIVE_PROCESS BELONGS_T
O_CO…
BELONGS_T O_COGNITIV…
BELONGS_T
O_…
BELONGS_…
BELONGS_T…
BELONGS_T…
BELONGS_T
O…
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CleanW…
Find and collect
sev…
Activate solar panels.
Build Wind Towers
Repair Water
Mills
Item Find
Activate Solar
Panels
Build
Wind Towers
Repair
Water Mills
Applying
Apply Choose
Construct Develop
Experim…
Identify
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Model Organize
Plan
Select
Solve
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Innov8
Games
Engineering
Games
Engineering
Serious Games
- Definition
- Kurze Geschichte Beispiele
- Barlock, Drink & Drive, Hord Battle III
- Spielemechaniken
Herausforderung: Gestaltung
- Agile Spieleentwicklung
- Details zu Hord Battle III
- Werkzeuge (EDoS, ATTAC-L)
- Spielelemente
Systematische Gestaltung
- Lerntheorien, Rahmenwerke, LM-GM framework
- Ontologie, RDF, OWL
- Ontologische Ableitung kognitiver Prozesse von Serious
Zusammen-
fassung
Games
Engineering
[1] J. Bu¨ttner, C. Merz, and S. v. Mammen. Horde battle iii or how to dismantle a swarm. In 2020 IEEE Conference on Games (CoG), pages 640–641, 2020.
[2] M. Ciolkowski, S. Faber, and S. von Mammen. 3-d visualization of dynamic runtime structures.
In Proceedings of the 27th International Workshop on Software Measurement and 12th
International Con-ference on Software Process and Product Measurement, pages 189–198, 2017.
[3] M. D¨aschinger, A. Knote, R. Green, and Sebastian von Mammen. A human-in-the-loop environment for developmental biology. In Artificial Life Conference Proceedings 14, pages 475–482. MIT Press, 2017.
[4] S. Edenhofer, S. R¨adler, M. Hoß, and S. von Mammen. Self-organised construction with revit.
In Proceedings of the 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems, pages 157–159, Augsburg, Germany, September 2016. IEEE Computer Society.
[5] M. K. Heinrich, S. von Mammen, D. N. Hofstadler, M. Wahby, P. Zahadat, T. Skrzypczak, M. D.
Soorati, R. Krela, W. Kwiatkowski, T. Schmickl, et al. Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics. Journal of the Royal Society Interface, 16(156):20190238, 2019.
[6] C. Jacob, S. von Mammen, T. Davison, A. Sarraf-Shirazi, V. Sarpe, A. Esmaeili, D. Phillips, I. Yaz- danbod, S. Novakowski, S. Steil, C. Gingras, H. Jamniczky, B. Hallgrimsson, and B. Wright. LINDSAY Virtual Human: Multi-scale, Agent-based, and Interactive, volume 422 of Advances in Intelligent Mod-elling and Simulation: Artificial Intelligence-based Models and Techniques in Scalable
Computing, pages 327–349. Springer Verlag, 2012.
[7] A. Knote, S. Fischer, S. Cussat-Blanc, F. Niebling, D. Bernard, F. Cogoni, and S. von Mammen.
Immersive analysis of 3d multi-cellular in-vitro and in-silico cell cultures. In 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), pages 82–827, Dec 2019.
[8] A. Knote and S. von Mammen. A model and pipeline for interactive simulation of morphological biology. In Morphogenetic Engineering Workshop, at the European Conference on Artificial Life (ECAL), 2017.
[9] A. Knote and S. von Mammen. Adaptation and integration of gpu-driven physics for a biology research ris. In 2018 IEEE 11th Workshop on Software Engineering and Architectures for Real- time Interactive Systems (SEARIS), pages 1–7, 2018.
[10] A. Knote and S. von Mammen. Interactive Agent-Based Biological Cell Simulations for Morphogenesis, chapter 9, pages 115–124. Kassel University Press, 2019.
[14] Y. Raies and S. von Mammen. A swarm grammar-based approach to virtual world generation. In Evo*
- evomusart: 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design, 2021.
[15] S. Rudolph, S. von Mammen, J. Jungbluth, and J. H¨ahner. Design and evaluation of an extended learning classifier-based starcraft micro ai. In European Conference on the Applications of Evolutionary Computation, pages 669–681. Springer, 2016.
[16] A. Sarraf Shirazi, T. Davison, S. von Mammen, J. Denzinger, and C. Jacob. Adaptive agent
abstractions to speed up spatial agent-based simulations. Simulation Modelling Practice and Theory, 40:144–160, 2014.
[17] J. Schikarski, O. Meisch, S. Edenhofer, and S. von Mammen. The digital aquarist: An interactive
ecology simulator. In Proceedings of the European Conference on Artificial Life 2015 (ECAL), pages 389–396, 2015.
[18] S. Truman, N. Rapp, D. Roth, and S. von Mammen. Rethinking real-time strategy games for virtual reality. In Proceedings of the 13th International Conference on the Foundations of Digital Games, pages 1–6, 2018.
[19] S. Truman and S. von Mammen. An integrated design of world-in-miniature navigation in virtual reality. In International Conference on the Foundations of Digital Games, FDG ’20, New York, NY, USA, 2020. Association for Computing Machinery.
[20] S. von Mammen. Interactive self-organisation. Habilitation Thesis, University of Augsburg, November 2016.
[21] S. von Mammen, T. Davison, H. Baghi, and C. Jacob. Component-based networking for simulations in medical education. In IEEE Symposium on Computers and Communications, ISCC’10, pages 975–
979, Riccione, Italy, 2010. IEEE Press.
[22] S. von Mammen and C. Jacob. Evolutionary swarm design of architectural idea models. In Genetic and Evolutionary Computation Conference (GECCO) 2008, pages 143–150, Atlanta, USA, 2008. ACM Press.
[23] S. von Mammen and C. Jacob. Swarm-driven idea models - from insect nests to modern architecture. In
C. Brebbia, editor, Eco-Architecture 2008, Second International Conference on Harmonisation
Between Architecture and Nature, pages 117–126, Algarve, Portugal, 2008. WIT Press.
Games
Engineering
[27]
S. von Mammen, D. Phillips, T. Davison, and C. Jacob. A graph-based developmental swarm
represen-tation and algorithm. In Swarm Intelligence, volume 6234 of Lecture Notes in Computer Science, pages 1–12, Brussels, Belgium, 2010. Springer Verlag.
[28]
S. von Mammen, S. Schellmoser, C. Jacob, and J. H¨ahner. The Digital Patient: Advancing
Medical Research, Education, and Practice, chapter 11. Modelling & Understanding the Human Body with Swarmscript, pages 149–170. Wiley Series in Modeling and Simulation. John Wiley &
Sons, Hoboken, New Jersey, 2016.
[29]
S. von Mammen and H.-G. Schmidt. Wenn eine miniatur laufen lernt. Bibliotheksforum Bayern, 13(4):250–253, 2019.
[30]
S. von Mammen and J.-P. Stegh¨ofer. Bring it on, complexity! present and future of self-organising middle-out abstraction. In The Computer after Me: Awareness and Self-Awareness in Autonomic Systems, pages 83–102. World Scientific, 2015.
[31]
S. von Mammen and J. Taron. A trans-disciplinary program for biomimetic computing and
architectural design. In 6th International Conference of the Arab Society for Computer Aided Architectural Design (ASCAAD), pages pp. 141–154, Manama, Bahrain, February 2012. ASCAAD.
[32]
S. von Mammen, S. Tomforde, and J. H¨ahner. An organic computing approach to self-organizing robot ensembles. Frontiers in Robotics and AI, 3:67, 2016.
[33]
S. von Mammen, D. Wagner, A. Knote, and U. Taskin. Interactive simulations of biohybrid systems. Frontiers in Robotics and AI, 4:50, 2017.
[34]
S. von Mammen, M. Weber, H. Opel, and T. Davison. Interactive multi-physics simulation for
endodon-tic treatment. In Modeling and Simulation in Medicine Symposium at SpringSim 2015, pages 36–41. Curran Associates, Inc., 2015.
[35]
D. Wagner, C. Hofmann, H. Hamann, and S. von Mammen. Design and exploration of braiding swarms in vr. In Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology, page 13, Gothenborg, Sweden, November 2017.
[36]
S. Wodarczyk and S. von Mammen. Emergent multiplayer games. In IEEE CoG ’20: Proceedings of the 2nd International Conference on Games, Osaka, Japan, in press 2020. IEEE.
[37]
P. Ziegler, D. Roth, A. Knote, M. Kreuzer, and S. von Mammen. Simulator sick but still immersed:
A comparison of head-object collision handling and their impact on fun, immersion, and simulator sickness. In 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pages 743–744.
IEEE, 2018.
[Aarseth et al.(2003)Aarseth, Smedstad, and Sunnan ̊a] Espen Aarseth, Solveig Marie Smedstad, and Lise Sunnan
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[Büttner et al.(2021)Büttner, Merz, and Mammen] Johannes Büttner, Christian Merz, and Sebastian von
Mammen. Playing with dynamic systems-battling swarms in virtual reality. In International Conference on the Applications of Evolutionary Computation (Part of EvoStar), pages 309–324. Springer, 2021.
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