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Master

Reference

Proposing a Framework for the analysis of integration in Serious Games

MANZONI, Alex

Abstract

The notion of integration in educational games, namely the way in which the learning content is inserted into a game for efficient learning and increased motivation, remains relatively undefined in the literature (Szilas & Acosta, 2011), and no operational guide to analyse existing educational games has been provided to date. The goal of this master thesis is to design a tool to objectively classify and quantify educational games based on the type and degree of integration between the game and its learning content. For this purpose, two questionnaires have been created, the first assessing the primary learning outcome (cognitive, motor, affective, communicative) targeted by the game being analyzed, and the other providing a guide to classify the game's type of integration based on the framework. The framework's implications for the notion of integration in educational games will be discussed, and propositions for future research on the topic will be provided.

MANZONI, Alex. Proposing a Framework for the analysis of integration in Serious Games. Master : Univ. Genève, 2017

Available at:

http://archive-ouverte.unige.ch/unige:98948

Disclaimer: layout of this document may differ from the published version.

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RESUME

The notion of integration in educational games, namely the way in which the learning content is inserted into a game for efficient learning and increased motivation, remains relatively undefined in the literature (Szilas & Acosta, 2011), and no operational guide to analyse existing educational games has been provided to date.

The goal of this master thesis is to design a tool to objectively classify and quantify

educational games based on the type and degree of integration between the game and its learning content.

For this purpose, two questionnaires have been created, the first assessing the primary learning outcome (cognitive, motor, affective, communicative) targeted by the game being analyzed, and the other providing a guide to classify the game’s type of integration based on the framework.

The framework’s implications for the notion of integration in educational games will be discussed, and propositions for future research on the topic will be provided.

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“Proposing​ ​a​ ​Framework​ ​for​ ​the​ ​analysis​ ​of integration​ ​in​ ​Serious​ ​Games”

Alex​​Manzoni Master​​Thesis

Master ​ ​ MALTT

​ ​ Volt ​ ​ - ​ ​ 2017

Final ​ ​ version

Jury:

Nicolas​​Szilas​​-​​Université​​de​​Genève Julien​​Da​​Costa​​-​​Université​​de​​Genève Denise​​Sutter​​Widmer​​-​​Université​​de​​Genève

Jacob​​Habgood​​-​​Sheffield​​Hallam​​University

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Summary:

The​​notion​​of​​integration​​in​​educational​​games,​​namely​​the​​way​​in​​which​​the​​learning​​content targeted​​is​​inserted​​into​​a​​game​​that​​is​​also​​supposed​​to​​be​​engaging​​for​​the​​player,​​remains relatively​​undefined​​and​​not​​clearly​​understood​​in​​the​​literature.​​Although​​attempts​​have been​​made​​by​​Malone​​(1981),​​Habgood​​(2005),​​Szilas​​&​​Acosta​​(2011)​​to​​offer​​suggestions for​​what​​kind​​of​​integration​​types​​are​​more​​appropriate​​than​​others,​​no​​attempts​​to​​provide an​​operational​​guide​​in​​order​​to​​effectively​​analyse​​(and​​even​​quantify)​​this​​concept​​on existing​​educational​​games​​have​​been​​provided​​to​​date.

The​​goal​​of​​this​​master​​thesis​​is​​to​​develop​​a​​framework​​to​​unify​​all​​the​​relevant​​contributions to​​this​​topic​​advanced​​in​​the​​literature​​(while​​at​​the​​same​​time​​adding​​new​​notions​​to

complete​​them),​​and​​to​​provide​​a​​tool​​to​​objectively​​categorise​​and​​quantify​​educational games​​based​​on​​the​​type​​and​​degree​​of​​integration​​between​​the​​game​​and​​its​​learning content.

For​​this​​purpose,​​two​​questionnaires​​have​​been​​created,​​the​​first​​assessing​​the​​primary learning​​outcome​​(cognitive,​​motor,​​affective,​​communicative)​​targeted​​by​​the​​game​​being analysed,​​and​​the​​second​​one​​providing​​a​​guide​​to​​categorise​​the​​game’s​​type​​of​​integration based​​on​​the​​framework.

The​​underlying​​assumption​​is​​that​​certain​​types​​of​​integration​​are​​more​​relevant​​than​​others depending​​on​​the​​learning​​outcomes​​targeted.

The​​framework’s​​implications​​for​​the​​notion​​of​​integration​​in​​educational​​games​​will​​be discussed,​​and​​propositions​​for​​future​​research​​on​​the​​topic​​will​​be​​provided.

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Index:

1.​​Introduction​………..……...4

1.​​1.​​The​​popularity​​of​​Videogames​​today………4

1.​​2.​​The​​current​​“Serious​​games”​​landscape………..5

1.​​3.​​The​​notion​​of​​Integration​​in​​educational​​games………..6

1.​​4.​​The​​first​​theoretical​​framework​​on​​integration:​​intrinsic​​fantasy.………...…...6

1.​​5.​​A​​different​​theoretical​​framework​​for​​integration:​​integration​​by​​game​​mechanics……...7

1.​​6.​​An​​experimental​​test​​of​​the​​efficiency​​of​​games​​integrated​​by​​the​​game​​mechanics.…..8

1.​​7.​​Secondary​​aspects:​​the​​“production​​values”​​and​​competition………..9

1.​​8.​​An​​attempt​​to​​classify​​games​​integrated​​by​​their​​mechanics………..10

1.​​9.​​Learning​​outcomes………13

2.​​Proposing​​a​​unifying​​framework​……….15

2.​​1.​​Unifying​​the​​contributions​​in​​the​​literature……….15

2.​​2.​​The​​benefits​​of​​the​​framework.………16

3.​​The​​elements​​of​​the​​framework​………...17

3.​​1.​​Mechanics​………..18

3.​​1.​​1.​​Systemic​​Learning……….18

3.​​1.​​2.​​Obstacles………22

3.​​1.​​3.​​Winning​​strategies……….24

3.​​1.​​4.​​Central​​and​​peripheric​​mechanics………..25

3.​​1,​​5.​​Difficulty………...……...26

3.​​1.​​6.​​Progression………....27

3.​​2.​​Media​​and​​Context……….………….29

3.​​2.​​1.​​Story,​​Fictional​​World,​​Images………...………….29

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4.​​1.​​How​​it​​works………32

4.​​2.​​The​​Items​​of​​the​​Learning​​Outcomes​​Questionnaire.………..33

4.​​3.​​The​​weightings​​for​​each​​element​​of​​the​​framework……….34

4.​​3.​​1.​​Primary​​learning​​outcome​​targeted:​​Cognitive…….……..….…….…….…….……….34

4.​​3.​​2.​​Primary​​learning​​outcome​​targeted:​​Motor…….…….…….….…….…….…….……...36

4.​​3.​​3.​​Primary​​learning​​outcome​​targeted:​​Affective…….………....…….…….…….………..38

4.​​3.​​4.​​Primary​​learning​​outcome​​targeted:​​Communicative………...…….…….…….……...40

5.​​The​​“Guide​​questions”​​questionnaire​………...42

5.​​1.​​How​​it​​works………42

5.​​2.​​Items​​of​​the​​“Guide​​questions”​​questionnaire………43

6.​​An​​Example​​of​​Analysis:​​Climate​​Challenge​………47

7.​​Discussion​​and​​Conclusions​………50

8.​​References​……….………53

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1. ​ ​ Introduction:

1.​​1.​​The​​popularity​​of​​Videogames​​today

Video​​games​​today​​are​​a​​booming​​industry,​​already​​generating​​more​​revenue​​each​​year than​​the​​music​​and​​movies​​industries.​​According​​to​​the​​2016​​Entertainment​​Software Association​​Report,​​48%​​of​​U.S.​​households​​own​​a​​game​​console​​and​​63%​​include​​at​​least one​​person​​who​​plays​​video​​games​​3​​hours​​or​​more​​per​​week.​​According​​to​​the​​same​​data, the​​US​​gamer​​demographic​​is​​progressively​​getting​​more​​diverse​​in​​terms​​of​​key​​variables such​​as​​gender​​or​​age,​​with​​the​​average​​game​​player​​being​​35​​years​​old,​​and​​women​​now being​​41%​​of​​the​​entire​​gamer​​population.​​Following​​the​​2017​​Ipsos​​“The​​New​​Faces​​of Gaming”​​report,​​similar​​results​​are​​found​​in​​Europe,​​with​​44%​​of​​gamers​​being​​female, spending​​an​​average​​of​​4.6​​hours​​per​​week​​on​​smartphones​​and​​tablet​​games​​alone.

Being​​an​​incredible​​source​​of​​technological​​innovation​​and​​considering​​the​​interest​​in​​the activity,​​video​​games​​can​​be​​used​​for​​a​​wide​​variety​​of​​goals,​​including​​education.​​Video games​​with​​an​​explicitly​​educational​​goal​​in​​mind​​have​​therefore​​tried​​to​​harness​​the potential​​and​​benefits​​of​​the​​technology,​​such​​as​​the​​ability​​to​​intrinsically​​motivate (Anyaegbu,​​Ting​​&​​Li,​​2012),​​to​​generate​​Flow​​states​​in​​which​​the​​user​​is​​completely immersed​​into​​the​​experience​​often​​losing​​the​​sense​​of​​self​​and​​sense​​of​​time​​(Annetta, 2010),​​to​​emotionally​​stimulate​​the​​user​​to​​include​​social​​activities​​such​​as​​competition (Landers​​&​​Callan,​​2011).

Among​​the​​first​​attempts​​at​​developing​​video​​games​​with​​educational​​goals,​​certain

examples​​can​​be​​mentioned​​such​​as​​a​​1971​​game​​called​​“The​​Oregon​​Trail”​​(a​​game​​where players​​experience​​the​​life​​of​​a​​group​​of​​19th-century​​settlers​​traveling​​along​​the​​Oregon Trail,),​​or​​a​​1973​​game​​called​​“Lemonade​​Stand”​​for​​the​​Apple​​2​​Computer​​(a​​business simulator​​where​​the​​player​​manages​​a​​lemonade​​stand​​and​​has​​to​​learn​​basic​​business variables​​such​​as​​the​​costs​​of​​the​​activities,​​the​​profits,​​trying​​to​​determine​​the​​price​​of​​every glass​​of​​lemonade,​​etc).

These​​first​​productions,​​although​​innovative​​for​​their​​time,​​were​​quite​​limited​​in​​terms​​of graphics,​​gameplay,​​and​​learning​​experience.​​Even​​considering​​their​​limitations,​​they represent​​an​​important​​bedrock​​and​​they​​were​​responsible​​for​​starting​​a​​trend​​that​​would bring​​us​​the​​more​​successful​​pedagogical​​games​​we​​have​​today.

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Following​​the​​popularity​​of​​successful​​games​​such​​as​​“Brain​​Training”​​for​​Nintendo​​DS,​​​​the production​​of​​what​​are​​now​​called​​“serious​​games”​​(pedagogical​​games​​with​​the​​explicit intent​​of​​teaching​​the​​players​​a​​specific​​content)​​started​​to​​expand​​to​​include​​a​​wide​​variety of​​domains,​​including​​highly​​specialised​​applications​​such​​as​​simulations​​in​​the​​medical​​field (for​​Parkinson’s​​or​​Alzheimer’s​​patients​​for​​example),​​using​​virtual​​reality,​​and​​other

innovative​​game​​design​​technologies.

1.​​2.​​The​​current​​“Serious​​games”​​landscape

According​​to​​a​​Report​​from​​Markets​​&​​Markets,​​“Serious​​games”​​today​​focus​​less​​of​​broader learning​​and​​instead​​target​​specific​​set​​of​​skills​​in​​various​​domains​​(often​​with​​the​​goal​​of training​​the​​users​​for​​specific​​tasks​​or​​jobs),​​such​​as​​​military,​​government,​​education, corporate,​​healthcare,​​media​​&​​advertising.​​With​​an​​estimated​​yearly​​growth​​for​​the​​entire industry​​of​​16%​​until​​2020​​and​​a​​revenue​​of​​5.2​​billion​​of​​euros​​every​​year,​​the​​corporate​​and educational​​domains​​in​​particular​​are​​the​​ones​​that​​are​​expected​​to​​grow​​the​​most​​for​​the next​​3​​years.

According​​to​​a​​Report​​from​​Sam​​Adkins​​(CEO​​of​​Metaari,​​an​​“ethics-based​​market​​research firm​​that​​identifies​​revenue​​opportunities​​for​​learning​​technology​​suppliers”)​​that​​analysed​​the Serious​​Game​​markets​​in​​the​​US​​specifically,​​the​​trends​​that​​are​​growing​​the​​most​​in​​this area​​are​​games​​dedicated​​to​​young​​children,​​and​​the​​ones​​using​​augmented​​reality​​and geolocation.

Furthermore,​​according​​to​​the​​same​​data,​​the​​general​​tendency​​for​​these​​products​​is​​to follow​​the​​mobile​​trend​​(which​​is​​expected​​to​​reach​​35%​​of​​the​​revenue​​from​​the​​entire E-learning​​market​​until​​2020)​​with​​many​​serious​​games​​being​​developed​​for​​smartphones and​​tablets​​exclusively.

Among​​the​​Industry​​leaders​​developing​​Serious​​games,​​“Serious​​Games​​International”

(based​​in​​the​​UK)​​and​​“Break​​Away​​games”​​(based​​in​​the​​US)​​can​​be​​mentioned,​​having created​​games​​such​​as​​“vHealthcare”​​(a​​game​​for​​healthcare​​professionals​​to​​better​​learn how​​to​​interact​​with​​patients,​​how​​to​​more​​efficiently​​perform​​their​​tasks,​​etc.),​​and​​“Evo”​​(a game​​where​​biology​​students​​learn​​how​​certain​​genetic​​traits​​in​​the​​Guinea​​Fish​​are​​passed from​​generation​​to​​generation).

With​​a​​serious​​game​​market​​so​​highly​​diversified,​​and​​many​​products​​that​​specifically​​target institutions’​​goals,​​it​​is​​difficult​​to​​generally​​assess​​the​​effectiveness​​of​​educational​​games compared​​to​​other​​mediums​​of​​learning​​such​​as​​texts,​​or​​educational​​videos.​​Existent​​meta

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analyses​​however,​​show​​promising​​results​​such​​as​​the​​ones​​for​​serious​​games​​in

educational​​contexts​​(​Backlund​​&​​Hendrix,​​2013;​​Randel,​​Morris,​​Wetzel​​&​​Whitehill,​​1992), and​​for​​the​​ones​​evaluating​​virtual​​reality​​(Virvou,​​Katsionis,​​&​​Manos,​​2005).​​More

specifically,​​these​​studies​​show​​that,​​on​​top​​of​​the​​better​​acquisition​​of​​the​​educational material,​​one​​of​​the​​biggest​​benefit​​users​​get​​from​​using​​a​​video​​game​​format​​for​​their

learning​​is​​motivational,​​improving​​the​​interest​​in​​the​​domain​​and​​wanting​​to​​continue​​to​​learn at​​a​​deeper​​level,​​compared​​to​​standard​​formats​​such​​as​​text​​or​​classroom​​explanations.

Moreover,​​the​​types​​of​​games​​that​​seem​​to​​lead​​to​​the​​most​​efficient​​acquisition​​of​​the learning​​content​​are​​the​​ones​​that​​target​​specific​​knowledge​​or​​skills.

1.​​3.​​The​​notion​​of​​Integration​​in​​educational​​games

Regardless​​of​​the​​type​​of​​serious​​game,​​its​​genre,​​its​​public​​targeted,​​its​​application, there​​is​​an​​important​​aspect​​to​​always​​consider​​when​​developing​​educational​​games:​​the notion​​of​​the​​articulation​​between​​the​​game​​itself​​and​​the​​learning​​content,​​meaning​​how​​the learning​​content​​is​​inserted​​into​​the​​game​​for​​efficient​​learning.

This​​aspect​​is​​central​​and​​it​​is​​supposed​​to​​allow​​the​​most​​effective​​acquisition​​of​​the

learning​​content​​targeted​​by​​the​​developers​​while​​capitalising​​on​​what​​makes​​games​​fun​​and interesting​​at​​the​​same​​time.

1.​​4.​​The​​first​​theoretical​​framework​​on​​integration:​​intrinsic​​fantasy.

The​​first​​contributions​​in​​the​​literature​​on​​this​​topic​​have​​been​​the​​ones​​that​​tried​​to​​clearly define​​this​​concept​​and​​to​​develop​​a​​theoretical​​framework​​around​​it​​in​​order​​to​​analyse pedagogical​​games​​under​​the​​lens​​of​​the​​integration​​between​​the​​learning​​content​​and​​the certain​​features​​in​​the​​game,​​such​​as​​the​​fictional​​world​​the​​game​​takes​​place.

Malone​​(1981)​​has​​developed​​the​​notion​​of​​“intrinsic”​​(or​​“endogenous”)​​fantasy,​​where​​the learning​​content​​is​​linked​​to​​the​​imaginary,​​fictional​​world​​of​​the​​game,​​and​​“extrinsic”​​(or

“exogenous”)​​fantasy,​​where​​the​​learning​​content​​and​​the​​fictional​​world​​are​​not​​related​​to each​​other.

The​​author​​makes​​the​​example​​of​​a​​game​​where​​the​​players​​are​​supposed​​to​​understand the​​concept​​of​​fractions,​​where​​they​​have​​to​​select​​the​​correct​​point​​on​​a​​line​​(for​​example​​in the​​middle​​of​​a​​line​​if​​it’s​​a​​“1/2”​​fraction),​​one​​version​​with​​intrinsic​​fantasy,​​where​​the

​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​

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content​​are​​related)​​and​​an​​extrinsic​​fantasy​​version​​where​​the​​fictional​​world​​is​​replaced with​​generic​​geometrical​​figures​​without​​any​​reference​​to​​any​​fantasy​​context.

The​​main​​hypothesis​​was​​that​​games​​with​​intrinsic​​fantasy​​would​​be​​more​​likely​​to​​increase student’s​​motivation​​and​​lead​​to​​better​​learning.

Malone​​has​​experimentally​​tested​​this​​hypothesis​​in​​a​​study​​where​​children​​played​​a​​version of​​the​​game​​about​​fractions​​mentioned​​integrated​​by​​intrinsic​​fantasy​​(a​​fictional​​world​​with darts)​​and​​an​​extrinsic​​one​​(with​​only​​geometrical​​figures),​​and​​they​​measured​​the​​duration of​​gameplay​​for​​each​​version.​​The​​results​​did​​not​​confirm​​the​​original​​hypothesis,​​since​​there was​​no​​difference​​in​​duration​​between​​the​​intrinsic​​version​​and​​the​​extrinsic​​one.

For​​male​​participants,​​there​​was​​no​​difference​​in​​duration,​​whereas​​the​​duration​​for​​females was​​even​​lower​​in​​the​​intrinsic​​version​​of​​the​​game​​(the​​opposite​​of​​what​​was​​expected).

Another​​study​​(Habgood,​​Ainsworth,​​Benford,​​2005)​​showed​​that​​children,​​if​​put​​in​​a condition​​where​​they​​had​​to​​imagine​​and​​create​​prototypes​​of​​games​​themselves,​​had​​a much​​greater​​tendency​​to​​develop​​games​​with​​extrinsic​​fantasy,​​which​​underlines​​the difficulty​​in​​conceiving​​and​​developing​​intrinsically​​integrated​​games.​​Furthermore,​​they seemed​​to​​positively​​evaluate​​games​​primarily​​based​​on​​other​​elements​​than​​their​​fictional world​​(such​​as​​the​​graphics,​​whether​​the​​game​​was​​“fun”,​​whether​​it​​was​​difficult),​​which seriously​​put​​into​​question​​the​​primacy​​of​​the​​fictional​​world​​as​​the​​central​​criterion​​to determine​​the​​effective​​integration​​of​​an​​educational​​game.

1.​​5.​​A​​different​​theoretical​​framework​​for​​integration:​​the​​integration​​by​​game mechanics

These​​studies​​pushed​​their​​authors​​to​​reject​​the​​idea​​that​​the​​fictional​​world​​was​​the​​central aspect​​when​​determining​​the​​degree​​of​​integration​​in​​a​​pedagogical​​game.

Malone​​himself​​proposed​​to​​focus​​on​​other​​elements,​​such​​as​​the​​mechanics​​of​​the​​game itself,​​namely​​the​​rules​​that​​determine​​the​​interactions​​allowed​​by​​the​​the​​game.

This​​approach​​has​​not​​been​​developed​​by​​Malone​​himself,​​but​​has​​allowed​​other​​authors​​to elaborate​​a​​much​​more​​complete​​theoretical​​framework​​to​​analyse​​the​​integration​​of

educational​​games.

Habgood​​and​​colleagues​​(Habgood,​​Ainsworth,​​2005),​​inspired​​by​​Malone’s​​conclusion, proposed​​therefore​​a​​theoretical​​framework​​around​​the​​notion​​of​​efficient​​games​​being integrated​​around​​the​​game​​mechanics​​and​​not​​around​​the​​fictional​​world.​​They​​define​​“core

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mechanics”​​as​​“the​​mechanism​​through​​which​​players​​make​​meaningful​​choices​​and​​arrive at​​a​​meaningful​​play​​experience”.

Without​​necessarily​​forgetting​​that​​the​​fictional​​world​​of​​the​​game​​has​​its​​importance​​in​​order to​​motivationally​​engage​​the​​players​​into​​completing​​the​​tasks​​required​​in​​the​​game,​​the

“core​​game​​mechanics”​​are​​a​​much​​more​​central​​element​​that​​will​​determine​​the​​success​​of any​​game​​(what​​makes​​the​​game​​“Super​​Mario”​​fun​​is​​the​​fact​​that​​jumping​​from​​platform​​to platform,​​avoiding​​enemies​​and​​interacting​​with​​the​​world​​is​​fun,​​regardless​​of​​its​​fictional world,​​which​​could​​be​​set​​in​​ancient​​Greece​​or​​the​​Moon,​​without​​necessarily​​losing​​most​​of its​​appeal).

Habgood​​(2007)​​adopts​​therefore​​the​​notion​​of​​intrinsic​​and​​extrinsic​​integration​​similar​​to that​​of​​Malone,​​but​​this​​time​​that​​refers​​to​​the​​mechanics​​as​​opposed​​to​​the​​fictional​​world.

As​​the​​authors​​themselves​​have​​explained,​​it​​is​​easier​​to​​define​​this​​notion​​as​​a​​negative,​​by taking​​the​​example​​of​​many​​limited​​educational​​games,​​that​​clearly​​show​​an​​extrinsic​​type​​of integration.​​For​​example​​platforming​​games​​with​​context​​information​​that​​is​​supposed​​to convey​​the​​learning​​content​​that​​appears​​between​​levels,​​or​​quizzes​​at​​certain​​points​​in​​the game.

Intrinsically​​integrated​​games​​should​​make​​sure​​that​​the​​player​​learns​​the​​mechanics themselves,​​and​​to​​insert​​the​​learning​​elements​​into​​the​​more​​“fun”​​elements​​of​​the​​game, avoiding​​a​​tension​​between​​the​​“fun”​​parts​​of​​the​​game,​​and​​the​​learning​​parts​​of​​a​​game.

1.​​6.​​An​​experimental​​test​​of​​the​​efficiency​​of​ ​games​​integrated​​by​​the​​game mechanics.

Habgood​​(2007)​​has​​tested​​the​​notion​​that​​the​​game​​mechanics​​(rather​​than​​the​​fictional world)​​represent​​a​​much​​more​​useful​​criterion​​to​​determine​​whether​​a​​game​​has​​an

efficiently​​integrated​​learning​​content.​​In​​order​​to​​do​​this,​​he​​developed​​an​​educational​​game called​​“Zombie​​Division”.

Zombie​​Division​​is​​a​​game​​about​​teaching​​its​​players​​to​​perform​​mathematical​​operations (more​​specifically,​​divisions)​​on​​the​​basis​​of​​certain​​divisors​​(such​​as​​2,​​5,​​or​​10).​​In​​the study,​​the​​goal​​is​​to​​compare​​two​​different​​versions​​of​​the​​game,​​an​​intrinsically​​integrated one,​​and​​an​​extrinsically​​integrated​​one.

In​​the​​intrinsic​​version,​​the​​player​​impersonates​​a​​knight​​that​​has​​to​​fight​​skeleton​​enemies, who​​have​​number​​attached​​to​​their​​chest,​​and​​the​​player​​has​​to​​choose​​the​​correct​​weapon among​​the​​available​​ones​​to​​defeat​​them​​(each​​weapon​​represents​​a​​different​​divider,​​such

​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​ ​​

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divisible,​​using​​the​​different​​divisors.​​The​​player​​advances​​after​​each​​level,​​has​​a​​fixed number​​of​​life​​points,​​and​​after​​each​​error​​(choosing​​the​​wrong​​weapon​​for​​a​​skeleton​​that wears​​a​​certain​​number),​​the​​player​​loses​​1​​life​​point,​​and​​once​​the​​life​​points​​reach​​0​​the player​​has​​to​​restart​​from​​the​​beginning​​of​​the​​level.​​In​​the​​extrinsic​​version​​the​​player​​fights the​​skeletons​​with​​standard​​“action”​​mechanics​​without​​any​​reference​​to​​mathematics,​​and performs​​divisions​​only​​at​​the​​end​​of​​every​​level,​​in​​a​​standard​​quiz​​format.​​Finally,​​there​​is an​​additional​​control​​version​​of​​the​​game​​without​​any​​reference​​to​​mathematics​​whatsoever.

It​​is​​important​​to​​note​​that​​in​​“Zombie​​Division”​​the​​fictional​​world​​is​​set​​in​​ancient​​Greece with​​fantasy​​elements​​such​​as​​living​​skeletons​​(the​​“zombies”),​​and​​the​​authors​​have​​created these​​three​​versions​​of​​the​​game​​based​​of​​an​​articulation​​around​​the​​game​​mechanics regardless​​of​​the​​fictional​​world.​​The​​fictional​​world​​could​​have​​been​​modified​​without necessarily​​having​​a​​big​​impact​​on​​the​​game​​mechanics.

In​​the​​study,​​three​​versions​​of​​Zombie​​Division​​were​​evaluated,​​utilising​​measures​​of learning​​(in​​order​​to​​evaluate​​whether​​a​​version​​of​​the​​game​​allowed​​better​​learning

compared​​to​​the​​others),​​and​​of​​motivation​​(the​​duration​​of​​gameplay​​with​​the​​version​​when the​​participants​​had​​the​​choice​​to​​switch​​to​​different​​version​​whenever​​they​​wanted)​​and interviews​​that​​showed​​what​​participants​​thought​​about​​the​​different​​versions​​of​​the​​game.

The​​results​​showed​​that​​the​​intrinsic​​version​​was​​chosen​​more​​often​​compared​​to​​the​​other versions​​of​​the​​game​​and​​this​​version​​lead​​to​​better​​short-term​​learning​​of​​the​​content​​(tested with​​a​​post-test​​right​​after​​the​​gameplay​​sessions)​​and​​a​​long-term​​learning​​as​​well​​(tested with​​a​​delayed​​test​​two​​weeks​​after​​the​​first​​gameplay​​session).

The​​main​​contribution​​of​​Habgood’s​​study​​is​​that​​it​​shows​​for​​the​​first​​time​​that​​an​​intrinsically integrate​​game​​(by​​its​​mechanics)​​can​​effectively​​lead​​to​​increased​​motivation​​(such​​as​​Flow states)​​and​​learning​​compared​​to​​an​​extrinsically​​integrated​​one.

Regarding​​the​​hypothesis​​of​​better​​learning​​for​​the​​intrinsically​​integrated​​version,​​although no​​differences​​were​​observed​​for​​short-term​​learning,​​the​​authors​​also​​showed​​that​​the benefits​​of​​the​​intrinsically​​integrated​​version​​concerning​​learning​​were​​primarily​​long​​term ones,​​as​​measured​​by​​the​​post-test​​questionnaire​​several​​weeks​​after​​the​​gameplay sessions.

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1.​​7.​​Secondary​​aspects:​​the​​“production​​values”​​and​​competition

The​​study​​also​​showed​​that​​other​​aspects​​of​​the​​game​​not​​necessarily​​related​​to​​the mechanics​​were​​considered​​determinant​​by​​the​​participants,​​as​​observed​​by​​the​​interviews with​​them.​​The​​players​​mentioned​​that​​elements​​such​​as​​the​​graphics​​and​​the​​3D​​world played​​a​​key​​role​​into​​the​​appreciation​​of​​the​​game​​by​​the​​participants.

These​​are​​usually​​referred​​to​​as​​the​​“production​​values”.

Social​​elements​​of​​comparison​​and​​competition​​(the​​participants​​often​​compared​​their​​scores to​​those​​of​​their​​peers​​and​​were​​motivated​​to​​do​​better)​​were​​also​​mentioned​​several​​times in​​the​​interviews,​​and​​were​​shown​​to​​increase​​motivation​​and​​the​​game’s​​appeal​​through sharing​​strategies​​and​​solutions​​for​​the​​tasks​​in​​the​​game.

These​​studies​​confirm​​the​​potential​​of​​pedagogical​​games​​that​​consider​​that​​an​​integration by​​the​​mechanics​​is​​the​​more​​appropriate​​way​​of​​inserting​​the​​learning​​content​​into​​an educational​​game,​​but​​also​​remind​​us​​that​​other​​factors​​such​​as​​the​​“production​​values”, competition,​​social​​comparison,​​or​​the​​fictional​​world​​(as​​seen​​in​​the​​“intrinsic​​fantasy”

approach​​by​​Malone)​​have​​their​​importance​​as​​well.

1.​​8.​​An​​attempt​​to​​classify​​games​​integrated​​by​​their​​mechanics

Following​​the​​mentioned​​attempts​​at​​assessing​​the​​motivational​​and​​educational​​benefits​​of pedagogical​​games,​​others​​studies​​have​​tried​​to​​lay​​out​​the​​groundwork​​for​​a​​classification​​of types​​of​​articulation​​between​​the​​game​​mechanics​​and​​its​​learning​​content,​​in​​order​​to​​allow for​​a​​systematic​​analysis​​and​​development​​of​​these​​type​​of​​games.

Szilas​​&​​Acosta​​(2011)​​have​​proposed​​to​​distinguish​​between​​mechanics​​based​​on

“Systemic​​learning”,​​“Winning​​strategies”,​​“Obstacles”,​​and​​“Contextual​​learning”.​​The authors​​cite​​a​​few​​examples​​of​​games​​for​​each​​type​​of​​integration​​and​​justify​​their​​choices with​​detailed​​explanations.

The​​first​​type​​of​​articulation​​between​​the​​game​​mechanics​​and​​the​​learning​​content​​is​​called

“systemic​​learning”​​and​​in​​this​​type​​of​​games​​the​​player​​effectively​​learns​​the​​mechanics themselves,​​the​​system​​of​​rules​​with​​which​​he​​interacts​​with​​for​​the​​duration​​of​​the​​gaming

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experience.​​The​​player​​constantly​​interacts​​with​​the​​system​​and​​he​​adjusts​​his​​actions​​based on​​the​​reactions​​from​​the​​system​​itself.

The​​example​​that​​the​​authors​​cite​​is​​that​​of​​“Energyville”,​​a​​game​​where​​the​​player​​needs​​to learn​​the​​game​​mechanics​​that​​govern​​the​​energy​​resources​​of​​a​​city​​where​​he​​is​​the​​major.

Certain​​actions​​(such​​as​​deciding​​to​​reduce​​CO2​​emissions)​​will​​have​​certain​​undesired​​or unforeseen​​consequences.​​What​​the​​player​​learns​​is​​the​​mechanics​​themselves,​​meaning the​​basic​​interactions​​between​​element​​in​​the​​game​​(what​​is​​the​​effect​​of​​environmental policies​​on​​the​​economy​​etc),​​without​​necessarily​​having​​to​​learn​​exactly​​what​​kind​​of​​actual policies​​a​​government​​can​​introduce.

The​​second​​type​​of​​articulation​​between​​the​​game​​mechanics​​and​​the​​learning​​content​​is​​an integration​​by​​“winning​​strategies”.​​The​​idea​​with​​these​​types​​of​​games​​is​​that​​the​​player needs​​to​​reach​​a​​specific​​goal​​set​​by​​the​​game​​and​​he​​has​​different​​available​​strategies​​to achieve​​it.​​By​​freely​​exploring​​the​​world​​and​​the​​mechanics,​​what​​he​​will​​learn​​is​​a​​specific strategy​​among​​the​​available​​ones​​to​​achieve​​the​​goal​​set​​by​​the​​game.​​The​​game​​gives​​the player​​feedback​​(which​​can​​be​​in​​a​​binary​​“right/wrong”​​format,​​or​​a​​more​​natural​​in-game type​​of​​feedback​​that​​avoids​​judgement).

The​​example​​the​​authors​​give​​for​​such​​type​​of​​integration​​is​​the​​game​​“Dimenxian”.​​In​​this game​​the​​player​​will​​have​​to​​utilise​​game​​interfaces​​in​​order​​to​​practice​​and​​learn

mathematical​​notions.​​For​​example,​​in​​order​​to​​find​​a​​meteorological​​station​​the​​player​​has​​to reach​​to​​advance​​to​​the​​next​​level,​​the​​player​​has​​to​​learn​​to​​use​​a​​coordinates​​interface.

The​​player​​has​​different​​strategies​​available​​to​​orient​​himself​​using​​this​​interface​​and​​find​​the station.​​In​​this​​type​​of​​articulation,​​the​​learning​​content​​itself​​is​​not​​the​​mechanics​​of​​the game​​but​​the​​correct​​strategy​​the​​player​​will​​have​​to​​use​​(and​​that​​the​​game​​has​​to​​reinforce thanks​​to​​appropriate​​feedback​​that​​will​​hopefully​​guide​​the​​player​​to​​the​​correct​​strategy choice).

The​​third​​type​​of​​integration​​identified​​by​​the​​authors​​is​​called​​interaction​​by​​“obstacles”.​​In this​​case,​​the​​learning​​content​​is​​inserted​​at​​certain​​moments​​in​​the​​game​​when​​the​​player will​​be​​in​​front​​of​​an​​“obstacles”​​in​​order​​to​​continue​​in​​the​​game.​​The​​obstacle​​will​​be comprised​​of​​a​​choice​​between​​different​​options​​(for​​example​​a​​multiple​​choice​​question).

The​​authors​​cite​​as​​an​​example​​of​​this​​type​​of​​articulation​​the​​game​​“Trivial​​Pursuit”,​​a​​Trivia game​​where​​the​​player​​has​​to​​answer​​multiple​​choice​​questions​​in​​various​​domains​​such​​as History​​or​​Literature​​and​​receives​​points​​every​​time​​he​​answers​​correctly.

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Although​​“Trivial​​Pursuit”​​can​​be​​classified​​as​​a​​game​​and​​includes​​motivational​​elements​​to keep​​the​​player​​engaged,​​it​​is​​not​​that​​different​​from​​standard​​multiple​​choice​​tests​​found​​in any​​school.​​Clearly​​this​​game​​does​​not​​utilise​​the​​potential​​of​​educational​​games​​for

improved​​learning​​experience​​and​​motivation.

This​​type​​of​​articulation​​is​​often​​used​​in​​many​​pedagogical​​games​​in​​what​​are​​usually referred​​to​​as​​“edutainment”​​games,​​among​​the​​first​​attempts​​at​​educational​​games,​​where the​​learning​​content​​is​​added​​as​​an​​inconvenient​​addition​​to​​a​​stand-alone​​game,​​where​​the game​​is​​visibly​​divided​​between​​the​​“fun”​​phase​​and​​the​​learning​​phase,​​creating​​an

unwelcomed​​tension​​and​​the​​risk​​of​​being​​labeled​​as​​“chocolate-covered​​broccoli”​​(Habgood, 2007).

However,​​this​​type​​of​​articulation,​​if​​efficiently​​implemented,​​allows​​for​​many​​different​​types of​​learning​​content​​to​​be​​conveniently​​put​​into​​a​​game​​format​​and​​for​​certain​​types​​of learning​​content​​it​​might​​be​​the​​only​​solution​​available.

The​​last​​type​​of​​integration​​identified​​by​​Szilas​​&​​Acosta​​(2011)​​is​​what​​is​​called​​a

“contextual​​coupling”,​​where​​the​​mechanics​​of​​the​​game​​can​​be​​completely​​unrelated​​to​​the learning​​content,​​where​​what​​the​​player​​is​​supposed​​to​​learn​​is​​disseminated​​in​​the​​context of​​the​​game,​​it​​can​​be​​found​​in​​the​​story,​​in​​pop-ups​​that​​the​​player​​can​​open​​(often​​times these​​pop-ups​​being​​optional).

The​​example​​the​​authors​​mention​​to​​illustrate​​the​​limits​​of​​this​​type​​of​​integration​​is​​the​​game

“Versailles”,​​a​​“point​​and​​click”​​educational​​game​​where​​the​​player​​has​​the​​option​​to​​click​​on certain​​objects​​in​​order​​to​​reveal​​information​​about​​the​​context​​the​​game​​(the​​learning content,​​which​​consists​​in​​a​​french​​history​​lesson).

This​​type​​of​​integration​​is​​clearly​​the​​weakest,​​since​​it​​doesn’t​​make​​sure​​that​​the​​content​​is actually​​absorbed​​by​​the​​player,​​and​​it​​creates​​an​​even​​bigger​​tension​​between​​the​​learning content​​and​​the​​“fun”​​portion​​of​​the​​game.

Clearly,​​although​​the​​role​​of​​the​​fictional​​world​​the​​game​​is​​set​​in​​cannot​​be​​underestimated (as​​said​​before,​​secondary​​elements​​such​​as​​the​​“production​​values”​​can​​be​​surprisingly important​​in​​determining​​the​​appeal​​of​​an​​educational​​game),​​this​​type​​of​​articulation​​should be​​avoided,​​in​​favor​​of​​“systemic​​learning”,​​“winning​​strategies”​​or​​at​​the​​very​​least

“obstacles”.

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Szilas​​&​​Sutter​​Widmer​​(2009)​​mentioned​​the​​notion​​of​​“temporality”,​​namely​​the​​idea​​that the​​contact​​with​​the​​learning​​content​​should​​happen​​at​​the​​same​​time​​than​​the​​more​​“fun”

elements​​in​​the​​game.

1.​​9.​​Learning​​outcomes:

The​​literature​​on​​integration​​in​​pedagogical​​games​​has​​always​​tried​​to​​evaluate​​this​​notion​​in games​​with​​the​​idea​​that​​certain​​game​​design​​choices​​are​​to​​be​​preferred​​when​​it​​comes​​to develop​​an​​efficiently​​integrated​​game.​​However,​​it​​is​​also​​clear​​that​​the​​effectiveness​​of integration​​and​​the​​type​​of​​integration​​that​​should​​be​​sought​​also​​depends​​on​​the​​type​​of game​​that​​is​​being​​evaluated.

For​​example,​​the​​same​​standards​​should​​not​​be​​used​​to​​evaluate​​a​​game​​about​​learning math​​and​​a​​game​​about​​practicing​​the​​movement​​of​​certain​​limbs​​for​​patients​​affected​​by Parkinson’s​​disease.

Evaluations​​and​​considerations​​about​​the​​type​​of​​integration​​in​​a​​game​​should​​therefore​​take into​​account​​what​​is​​usually​​referred​​to​​as​​“Learning​​outcomes”,​​meaning​​the​​type​​of​​learning content​​that​​is​​targeted​​by​​the​​game.

The​​first​​attempt​​at​​classifying​​learning​​outcomes​​can​​be​​retraced​​to​​Bloom​​&​​Gagné​​(1978) and​​these​​were​​identified​​as​​being​​of​​cognitive​​nature,​​outlining​​the​​different​​components​​of such​​learning​​outcomes,​​such​​as​​the​​“content​​of​​the​​task”​​(basic​​knowledge​​the​​learner​​has to​​acquire​​in​​order​​to​​perform​​the​​relevant​​operations)​​and​​the​​intellectual​​“operations”​​to perform​​on​​such​​content.​​​​Bloom​​&​​Gagné​​further​​divide​​such​​content​​in​​“facts”,​​“concepts”

(categories​​of​​objects),​​principles​​(indicating​​the​​relationship​​between​​different​​objects)​​and procedures​​(motor​​procedures).

The​​intellectual​​operations​​that​​can​​be​​performed​​once​​learned​​are​​of​​different​​types,​​such as​​retelling​​the​​information​​by​​heart​​(“Memorisation”),​​paraphrasing​​the​​information

(“Summarisation”),​​the​​information​​can​​be​​used​​to​​anticipate​​certain​​outcomes​​(“Prediction”) or​​used​​in​​a​​certain​​way​​in​​order​​to​​produced​​a​​specific​​result​​(“Application”).

This​​learning​​outcomes’​​taxonomy​​of​​Bloom’s​​&​​Gagné​​(1987)​​was​​subsequently​​modified by​​Krathwohl​​(2002),​​who​​added​​an​​affective​​dimension​​to​​the​​framework.​​​​He​​defines​​this emotional​​type​​of​​learning​​outcome​​as​​follows:

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“Affective​​learning​​is​​demonstrated​​by​​behaviors​​indicating​​attitudes​​of​​awareness,​​interest, attention,​​concern,​​and​​responsibility,​​ability​​to​​listen​​and​​respond​​in​​interactions​​with​​others, and​​ability​​to​​demonstrate​​those​​attitudinal​​characteristics​​or​​values​​which​​are​​appropriate​​to the​​test​​situation​​and​​the​​field​​of​​study”

In​​order​​to​​further​​refine​​the​​taxonomy​​of​​learning​​outcomes​​in​​the​​literature,​​Baker​​&​​Mayer (1999)​​added​​a​​new​​social​​dimension,​​mentioning​​that​​in​​certain​​contexts,​​“communication skills”,​​“self-regulation”​​and​​“teamwork”​​are​​essential​​outcomes​​of​​the​​learning​​experience.

All​​of​​these​​approaches​​that​​focused​​on​​specifics​​types​​of​​learning​​outcomes​​(cognitive, affective,​​social)​​were​​summarized​​by​​Wouters,​​van​​der​​Spek,​​&​​van​​Oostendorp​​(2009)​​who proposed​​a​​unifying​​framework​​that​​classifies​​learning​​outcomes​​in​​the​​domain​​of

pedagogical​​games​​into​​4​​main​​categories,​​“cognitive”,​​“motor”,​​“affective”,​​and

“communicative”.

Each​​learning​​outcome​​category​​can​​be​​further​​divided​​in​​different​​sub-categories.​​For example,​​regarding​​the​​cognitive​​learning​​outcome,​​this​​outcome​​can​​be​​divided​​in

declarative​​knowledge​​that​​is​​either​​textual​​(such​​as​​an​​explanation​​of​​how​​an​​atom​​works) or​​non-textual​​(such​​as​​a​​3D​​image​​of​​an​​atom),​​and​​skills​​such​​as​​problem​​solving,​​decision making,​​and​​situational​​awareness.

Motor​​outcomes​​can​​be​​divided​​in​​acquisition​​or​​motor​​skills​​(for​​example​​acquiring​​the motor​​skills​​required​​to​​throw​​a​​basketball​​into​​the​​hoop)​​and​​compilation​​of​​such​​motor​​skills (integrating​​different​​skills,​​the​​old​​ones​​being​​integrated​​in​​the​​new​​ones)

Affective​​outcomes​​can​​be​​divided​​in​​attitude​​(for​​example​​a​​game​​that​​is​​supposed​​to

change​​the​​player’s​​attitude​​regarding​​a​​topic)​​and​​motivation​​(increasing​​the​​motivation​​for​​a specific​​domain​​targeted​​by​​the​​game).

Communicative​​outcomes​​can​​be​​divided​​in​​communicative​​skills,​​cooperation​​skills (learning​​to​​better​​cooperate​​in​​a​​board​​game​​for​​example)​​and​​negotiation.

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Figure1:LearningoutcomesaccordingtoWouters,vanderSpek,&vanOostendorp(2009).

Thus​​far,​​no​​attempt​​to​​include​​the​​notion​​of​​learning​​outcomes​​in​​an​​evaluation​​of​​the integration​​in​​pedagogical​​games​​has​​been​​observed​​in​​the​​literature.

2. ​ ​ Proposing ​ ​ a ​ ​ unifying ​ ​ Framework:

2.​​1.​​Unifying​​the​​contributions​​in​​the​​literature

As​​we​​have​​seen​​in​​the​​previous​​chapter,​​there​​have​​been​​efforts​​in​​the​​literature​​to​​clarify the​​notion​​of​​integration​​is​​pedagogical​​games,​​proposing​​classifications​​that​​each​​adopt different​​underlying​​assumptions​​(such​​as​​the​​fact​​that​​game​​mechanics​​are​​a​​more​​central aspect​​of​​games​​than​​the​​fictional​​world​​where​​they​​take​​place).

Despite​​the​​progress,​​there​​are​​no​​actual​​specific​​frameworks​​that​​try​​to​​unify​​these​​claims and​​approaches​​into​​an​​operational​​guide.

Students​​attempting​​to​​learn​​about​​the​​notion​​of​​integration​​in​​pedagogical​​games​​are confronted​​with​​a​​limited​​body​​of​​literature​​that​​is​​often​​times​​too​​complex​​and​​highly theoretical,​​which​​makes​​it​​hard​​for​​them​​to​​effectively​​absorb​​the​​material.

Although​​the​​relevant​​papers​​have​​cited​​several​​existing​​serious​​games​​to​​anchor​​the discussion​​to​​concrete​​real​​life​​examples​​(Habgood​​2007,​​Malone,​​1981;​​Szila​​&​​Acosta,

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2011;​​Szilas​​&​​Sutter​​Widmer,​​2009),​​no​​practical​​tool​​to​​categorize​​or​​systematically analyse​​games​​exists​​today.

An​​effort​​must​​therefore​​be​​made​​in​​order​​to​​unify​​these​​approaches​​and​​theoretical frameworks,​​in​​order​​to​​design​​a​​standardised​​tool​​(such​​as​​a​​questionnaire​​or​​an application)​​that​​is​​able​​to​​be​​used​​as​​an​​operational​​guide​​to​​analyse​​the​​integration​​in serious​​games​​in​​the​​most​​straightforward​​manner​​possible.

2.​​2.​​The​​benefits​​of​​the​​framework.

The​​benefits​​of​​using​​such​​a​​framework​​should​​be​​two-fold.​​First​​of​​all,​​although​​some knowledge​​on​​basic​​concepts​​(such​​as​​game​​mechanics)​​will​​probably​​be​​required​​in​​order to​​use​​the​​tool,​​the​​tool​​should​​be​​able​​to​​be​​used​​by​​students​​who​​haven’t​​reached

expertise​​on​​the​​topic​​yet.

Secondly,​​on​​top​​of​​being​​an​​operational​​guide,​​the​​tool​​will​​be​​an​​instrument​​which​​clarifies doubts​​on​​certain​​concepts​​that​​are​​perhaps​​too​​difficult​​to​​fully​​grasp​​by​​simply​​reading about​​them​​in​​the​​relevant​​papers.​​For​​example,​​a​​student​​might​​have​​read​​the​​literature​​on the​​difference​​between​​“obstacles”​​and​​“systemic​​learning”,​​but​​only​​by​​classifying​​a​​game answering​​to​​specific​​questions​​will​​he​​be​​able​​to​​fully​​comprehend​​these​​concepts​​that​​he had​​only​​vaguely​​theoretically​​understood​​up​​until​​that​​point.

The​​framework​​will​​try​​to​​unify​​existing​​concepts,​​theories,​​approaches,​​will​​be​​grounded​​in the​​current​​literature,​​but​​will​​also​​try​​to​​introduce​​novel​​concepts​​and​​ideas​​on​​how​​to​​think about​​integration​​in​​serious​​games.

Users​​who​​will​​benefit​​from​​the​​use​​of​​this​​framework​​will​​be​​of​​two​​kinds.​​Firstly,​​the framework​​will​​be​​useful​​for​​students​​who​​want​​to​​be​​able​​to​​analyse​​games​​and​​to

categorise​​them,​​such​​as​​university​​students​​or​​any​​subject​​being​​interested​​in​​the​​subject related​​to​​serious​​games.

Secondly,​​the​​framework​​will​​be​​useful​​for​​developers​​who​​are​​actually​​creating​​serious games.​​Both​​during​​the​​design​​and​​realisation​​phases,​​the​​developers​​will​​be​​able​​to​​use​​the framework​​as​​a​​guide​​to​​create​​games​​that​​are​​more​​integrated,​​allowing​​their​​product​​to​​be more​​appealing​​and​​useful.

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3. ​ ​ The ​ ​ Elements ​ ​ of ​ ​ the ​ ​ framework

The​​logic​​of​​the​​framework​​is​​that​​it​​will​​produce​​outputs​​in​​terms​​of​​typology​​(it​​will​​specify​​to which​​category​​of​​integration​​the​​game​​belongs)​​and​​in​​terms​​of​​a​​final,​​general​​score​​of integration​​for​​each​​game.

The​​final​​score​​will​​depend​​on​​the​​type​​of​​game​​​​that​​is​​being​​analysed.​​Different​​typologies of​​games​​will​​have​​different​​weighting​​of​​each​​framework​​element.​​For​​example,​​a​​game about​​learning​​mathematics​​will​​not​​have​​the​​same​​weighting​​of​​each​​framework​​element than​​a​​game​​about​​learning​​to​​move​​the​​arms​​for​​Parkinson’s​​disease​​patients.​​These different​​types​​of​​games​​will​​be​​divided​​based​​on​​their​​learning​​outcomes.

A​​questionnaire​​will​​be​​used​​to​​determine​​these​​learning​​outcomes​​for​​each​​game​​that needs​​to​​be​​analysed.​​The​​different​​learning​​outcomes​​are​​“Cognitive”,​​“Motor”,​​“Affective”,

“Communicative”​​based​​on​​the​​classification​​of​​Wouters,​​van​​der​​Spek,​​&​​van​​Oostendorp (2009).

This​​questionnaire​​will​​be​​administered​​to​​the​​expert​​who​​needs​​to​​analyse​​the​​game himself/herself,​​or​​to​​the​​corporate​​sponsor​​of​​the​​game​​if​​the​​framework​​is​​used​​by developers.

Once​​determined​​the​​learning​​outcomes,​​another​​questionnaire​​will​​be​​used​​to​​classify​​the game​​based​​on​​the​​different​​types​​of​​integration​​and​​other​​criteria​​of​​the​​framework.

The​​weighting​​of​​each​​framework​​element​​to​​determine​​the​​final​​score​​will​​be​​determined based​​on​​the​​results​​of​​the​​learning​​outcomes​​questionnaire.

This​​framework​​is​​divided​​in​​3​​main​​groups​​of​​types​​of​​integration,​​namely​​the​​“Mechanics”,

“Media”,​​“Context”.

The​​group​​called​​“Mechanics”​​is​​based​​on​​the​​classification​​seen​​on​​Szilas​​et​​Acosta​​(2011), which​​makes​​a​​distinction​​between​​integration​​by​​“Obstacles”,​​“Systemic​​Learning”,​​“Winning Strategies”,​​and​​“Contextual​​Learning”,​​and​​expand​​on​​it​​offering​​further​​sub-categories.

In​​the​​“Mechanics”​​category,​​the​​concepts​​of​​Difficulty​​and​​Progression​​of​​the​​game mechanics​​are​​introduced.

The​​group​​called​​Called​​“Media”​​refers​​to​​the​​nature​​of​​the​​images​​and​​sounds​​related​​to learning​​content​​used​​in​​the​​game.

Finally,​​the​​group​​called​​“Context”​​refers​​to​​the​​Story​​and​​fictional​​world​​of​​the​​game.

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Certain​​categories​​(for​​example,​​“Systemic​​Learning”)​​are​​divided​​in​​other​​sub-categories (for​​example​​“Necessity​​of​​learning”),​​and​​for​​each​​subcategory,​​the​​game​​can​​be​​classified according​​to​​a​​binary​​choice​​(for​​example,​​“Learning​​required”​​or​​“learning​​not​​required”).

For​​each​​binary​​choice,​​one​​of​​the​​options​​makes​​the​​game​​more​​integrated,​​the​​other​​less integrated.

Figure2:TheFramework.

Colors:

-Blue:Threemaintypesofintegration(“Media”,“Mechanics”,“Context”) -Orange:Subcategories

-Yellow:Optionsforthebinarychoices

Here​​is​​an​​explanation​​for​​each​​category​​and​​sub-category​​of​​the​​framework.

3. ​ ​ 1. ​ ​ Mechanics:

3.​​1.​​1.​​Systemic​​Learning

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In​​this​​type​​of​​integration​​the​​player​​is​​supposed​​to​​learn​​the​​game​​mechanics​​themselves, learning​​them​​while​​he​​plays,​​interacting​​with​​the​​elements​​of​​the​​game.​​An​​example​​of​​such game​​is​​Climate​​challenge,​​where​​the​​player​​impersonates​​a​​fictional​​“president​​of​​Europe”

and​​has​​to​​enact​​policies​​that​​are​​supposed​​to​​reduce​​and​​tackle​​climate​​change.​​The​​player chooses​​different​​cards​​that​​activate​​certain​​policies​​related​​to​​the​​various​​categories,​​such as​​economic,​​societal,​​related​​to​​energy,​​related​​to​​prime​​resources,​​and​​so​​on.

Slowing​​down​​climate​​change​​will​​come​​with​​certain​​consequences​​to​​other​​categories​​in​​the game,​​with​​the​​idea​​of​​a​​necessary​​sacrifice,​​compromise​​between​​the​​various​​components of​​the​​game.​​For​​example​​the​​player​​learns​​by​​interacting​​with​​the​​game,​​that​​certain components​​of​​the​​game​​can​​be​​influenced​​by​​other​​ones,​​such​​as​​learning​​how​​choosing policies​​to​​reduce​​CO2​​emissions​​will​​have​​an​​impact​​on​​certain​​resources​​available​​and​​a chance​​of​​the​​player’s​​reelection​​as​​president​​in​​the​​game.

The​​idea​​is​​that​​the​​understanding​​of​​the​​game’s​​mechanics​​that​​the​​player​​acquires​​can​​be transferred​​to​​what​​we​​call​​the​​“system​​of​​reference”,​​in​​the​​case​​of​​Climate​​Challenge​​it’s the​​actual​​policies​​of​​various​​governments​​to​​tackle​​climate​​change​​and​​their​​real

implications​​and​​consequences.

Once​​it’s​​established​​(with​​the​​use​​of​​the​​framework)​​that​​the​​game​​that​​is​​being​​evaluated has​​its​​learning​​content​​integrated​​by​​systemic​​learning,​​there​​are​​subcategories​​of​​this​​type of​​integration:

First​​of​​all,​​we​​have​​a​​binary​​choice​​between​​a​​game​​that​​requires​​the​​system​​to​​be

effectively​​learned​​to​​advance​​in​​the​​game,​​and​​a​​game​​that​​doesn’t​​make​​sure​​in​​any​​way that​​the​​player​​has​​acquired​​the​​mechanics​​of​​the​​game​​(so​​it​​let’s​​the​​player​​finish​​the​​game without​​having​​acquired​​and​​understood​​the​​learning​​content).

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