an ontology engineering approach to gamify collaborative learning scenarios

An Ontology Engineering Approach to Gamify Collabora7ve Learning Scenarios

Geiser Chalco1, Dilvan A. Moreira1, Riichiro Mizoguchi2, and Seiji Isotani1

20th Interna@onal Conference on Collabora@on and Technology, 2014

1University of São Paulo, Brazil. [email protected], {dilvan, sisotani}@icmc.usp.br

2Japan Advanced Ins@tute of Science and Technology, Japan. [email protected]

•  Introduc@on: – Concept of Gamifica@on – Problem: Gamifica@on in CL Scenarios

•  Approach: Ontological Engineering – Ontological Structure to Describe Gamified CL Scenarios

•  Applica@on of Ontological Structure •  Conclusions and Future Research

Outline

Sept. 9, 2014 An Ontology to Gamify CL Scenarios 2

Games are part of our life




Source: Entertainment SoUware Associa@on's (2014) & Huffingtonpost (2014)

Why does everyone enjoy playing games?


Engagement

Sa@sfac@on of psychological needs

Solve problems



Engagement


Solve problems

Games are problem-‐solving ac7vi7es (finding a way to destroy the other)

(feeling successful)



Engagement


Solve problems



Games are problem-‐solving ac7vi7es approached with a playful a@tude (voluntary)

Game Elements (endogenous)



Engagement


Solve problems



Games are problem-‐solving ac7vi7es approached with a playful a@tude (voluntary)

Game Elements (endogenous)

must probably solve more problems to gain points and to feel more

successful

Leaderboards

What is Gamifica@on?


The use of game design elements in non-‐game contexts (Deterding et al. 2011)

Playful Design Pervasive Games

Serious Games Gamifica@on





Serious Games Gamifica@on Gaming

Playing -‐ Playfulness

Rule-‐base systems that are design to

be playful








be playful

Elements Whole

How to use game elements in non-‐game applica@on








be playful

Elements Whole

How to use game elements in non-‐game applica@on Design of: •  Game interface •  Game mechanics •  …

Does Gamifica@on really work?




Many uses of gamifica@on are incorrect or poorly designed (Webb, 2013)




Reason: misconcep@on about what mo7vates a person to con@nue using a gamified applica@on




Reason: misconcep@on about what mo@vates a person to con@nue using a game or gamified applica@on





(Hamari, et al. 2014)

Results Nro Studies

All tests posi@ve 2

Posi7ve results in part of the tests

13

Only descrip@ve 7






Results Nro Studies

All tests posi@ve 2


13

Only descrip@ve 7

Context is an essencial






Results Nro Studies

All tests posi@ve 2


13

Only descrip@ve 7


Problem: Gamifica@on in CL Scenarios

Sept. 9, 2014 An Ontology to Gamify CL Scenarios

Instruc@onal Designer

CSCL Script (IMS-‐LD)

CL Ac@vi@es (interac@ons)

CL Scenario

LA LB

LC

29






CL Scenario

LA LB

LC

demo7va7on

•  Neglect his personal skills to complete a task as it was requested •  Sense of obliga@on imposes by the script

30






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics

nega7ve learning outcomes

+ demo7va7on


31






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics


+ demo7va7on


Efficien

t+

Bene

ficial +

32






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics


+ demo7va7on


Efficien

t+

Bene

ficial +

33






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics


+ demo7va7on


Efficien

t+

Bene

ficial +

I don’t want to game points… I want to

34






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics


+ demo7va7on


Efficien

t+

Bene

ficial +


Make social connec@on

35






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics


+ demo7va7on


Efficien

t+

Bene

ficial +



Explore new challenges

36






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics


+ demo7va7on


Efficien

t+

Bene

ficial +




WIN!

37






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics


+ demo7va7on


Efficien

t+

Bene

ficial +




WIN!

38






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics


+ demo7va7on


Efficien

t+

Bene

ficial +




WIN!

Tests

39






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics


+ demo7va7on


Efficien

t+

Bene

ficial +




WIN!

Tests

40






CL Scenario

LA LB

LC

nega@ve aitudes

degrade dynamics


+ demo7va7on


Efficien

t+

Bene

ficial +




WIN!

Tests

Context and needs of learners change

41

Approach: Ontological Engineering


Ontologies (Structures)

Knowledge Bases

(Models)

Best Prac7ces and Theories of Gamifica7on

Describe informa@on

Support for the development of

Instr. designer/ teacher/researcher

Ø design gamified CL scenarios Ø evaluate the prac@ces and theories Ø es@mate and calculate the benefits

Ontology-‐aware systems

From CL Scenario To Gamified CL Scenario


Gamifed CL Scenario

CL Scenario

Y<=I-‐goal

I-‐goal

W(A)-‐goal

Learning Strategy *

Learner I-‐role

Learner You-‐role

I-‐goal

CL process *

G

W(L)-‐goal Common goal

*

Interact Parern Teaching-‐learning process

*



Gamifed CL Scenario

Modeling of Learner's

Development

Learner Growth Model

(Isotani & Mizoguchi, 2007)

CL Scenario

Y<=I-‐goal

I-‐goal

W(A)-‐goal

Learning Strategy *

Learner I-‐role

Learner You-‐role

I-‐goal

CL process *

G


*


*



Gamifed CL Scenario


Development



Formation of Effective

Groups

Learners

Effec@ve Groups

(Isotani et al., 2009) CL Scenario

Y<=I-‐goal

I-‐goal

W(A)-‐goal

Learning Strategy *

Learner I-‐role

Learner You-‐role

I-‐goal

CL process *

G


*


*



Gamifed CL Scenario


Development




Groups

Learners

Effec@ve Groups

(Isotani et al., 2009)

Design of CL Scenarios

LA LB

Tutor Tutee

ü Learning Strategies ü Learning Goals ü Group Goals ü Roles

(Isotani et al., 2013)

CL Scenario

Y<=I-‐goal

I-‐goal

W(A)-‐goal

Learning Strategy *

Learner I-‐role

Learner You-‐role

I-‐goal

CL process *

G


*


*


47 Sept. 9, 2014


Development


Groups


Apply Gamification

CL Scenario

Y<=I-‐goal

I-‐goal

W(A)-‐goal

Learning Strategy *

Learner I-‐role

Learner You-‐role

I-‐goal

CL process *

G


*


*

LA LB

Tutor Tutee

“the use of game design elements in non-‐game …”

Gamifed CL Scenario

An Ontology to Gamify CL Scenarios

Unlockable System

Point System

Badge System


48 Sept. 9, 2014


Development


Groups


Apply Gamification

CL Scenario

Y<=I-‐goal

I-‐goal

W(A)-‐goal

Learning Strategy *

Learner I-‐role

Learner You-‐role

I-‐goal

CL process *

G


*


*

LA LB

Tutor Tutee

“the use of game design elements in non-‐game …”

•  What game mechanics must be used to mo7vate the learners?

Gamifed CL Scenario

Only: design of game mechanics

An Ontology to Gamify CL Scenarios

Unlockable System

Point System

Badge System

LA LB

Mo@va@on and Game Mechanics


Mo@va@on is the process used to allocate energy to maximize the sa7sfac7on of needs (Pritchard & Ashwood, 2008).

Behavior

Needs Satisfaction

LA LB




Behavior

Needs Satisfaction

Game elements

LA LB




Behavior

Needs Satisfaction

Game elements

change or intensify his needs

LA LB




Behavior

Needs Satisfaction

Game elements


The proper combina@on of different game elements provides an adequate environment for all learners.

LA LB




Behavior

Needs Satisfaction

Game elements



•  Game mechanics(GM): inputs (CL scenario) -‐> output (gaming)

Game mechanics

LA LB




Behavior

Needs Satisfaction

Game elements




Game mechanics

Point System

LA LB




Behavior

Needs Satisfaction

Game elements




Game mechanics

Point System Social Connec@on

LA LB




Behavior

Needs Satisfaction

Game elements




Game mechanics


demonstrate mastery relatedness

LA LB




Behavior

Needs Satisfaction

Game elements




Game mechanics


demonstrate mastery relatedness Every GM is Customized (ind.needs) -‐> Increase his liking for CL Ac@vi@es

Internaliza@on of mo@va@on

I-‐mot goal: Individual mo@va@onal goal


Behavior

Needs Satisfaction

Game elements

Ind. motivational goal


Sa@sfac@on of needs

*

An individual mo7va7onal goal represents the expected changes on the mind of a learner using game mechanics.

*

Game mechanics



Behavior

Needs Satisfaction

Game elements



Sa@sfac@on of needs

*


*

•  Amo@va@on -‐> External Reg. •  (External Mot. -‐> Intrinsic Mot.)

•  External Reg. -‐> Introjected Reg. •  Introjected Reg. -‐> Iden@fied Reg. •  Iden@fied Reg. -‐> Integrated Reg. •  Integrated Reg. -‐> Intrinsic Reg.

Self-‐determina@on Theory (Deci & Ryan, 2010)

Game mechanics



Behavior

Needs Satisfaction

Game elements



Sa@sfac@on of needs

*


•  Sa@sfac@on of Autonomy •  Sa@sfac@on of Relatedness •  Sa@sfac@on of Competence

•  Sa@sfac@on of Purpose •  Sa@sfac@on of Mastery

*

•  Amo@va@on -‐> External Reg. •  (External Mot. -‐> Intrinsic Mot.)

•  External Reg. -‐> Introjected Reg. •  Introjected Reg. -‐> Iden@fied Reg. •  Iden@fied Reg. -‐> Integrated Reg. •  Integrated Reg. -‐> Intrinsic Reg.


Self-‐determina@on Theory (Deci & Ryan, 2010) Pink Dan (2011)

Competence Relatedness Autonomy Purpose

Autonomy Mastery

Game mechanics

Player Role: Playing style (Ind. Pers. Trait)


Behavior

Needs Satisfaction

Game elements

Game mechanics

Ind. motivation goal

Playing styles (Ind. traits) Models of:

•  7 Player Types (Laws, 2002) •  4 Player Types (Bartle, 2004) •  8 Player Types (Marczewski, 2013) …



Behavior

Needs Satisfaction

Game elements

Game mechanics




The playing styles are individual personality traits that define the preferences of a person when he/she is playing a game.



Behavior

Needs Satisfaction

Game elements

Game mechanics




The playing styles are individual personality traits that define the preferences of a person when he/she is playing a game.

user-‐orienta5on (interac5ng with others)

system-‐orienta5on (exploring the game)

Interac5on-‐orienta5on (interac5ng inside)

act-‐orienta5on (having influence outside)

(Socializer)

(Explorer) (Achiever)

(Killer)

(Bartle, 2004) 4 Player Types

Player Role: Models of player types


Behavior

Needs Satisfaction

Game elements

Game mechanics


Playing styles (Ind. traits)

LA LB

Player Role

Mo@va@on stage Necessary condi@on

*

Psychological need Necessary condi@on

*

Playing style Desired condi@on

* Player Role

A Player Role defines the necessary and desired condi7ons that a learner must have to play a player type.

Y<=I-‐mot goal: Mo@va@onal strategy


Behavior

Needs Satisfaction

Game elements

Game mechanics



LA LB

Player Role

The Y<=I-‐mot goal is the mo7va7onal strategy that will be used by game elements to enhance the learning strategy employed by a learner in focus.

Motivational Strategy

Y<=I-‐mot goal

Enhance (learning strategy)



Behavior

Needs Satisfaction

Game elements

Game mechanics



LA LB

Player Role



Y<=I-‐mot goal


influence the de

sign of



Behavior

Needs Satisfaction

Game elements

Game mechanics



LA LB

Player Role



Y<=I-‐mot goal


Learner I-‐player role

influence the de

sign of



Behavior

Needs Satisfaction

Game elements

Game mechanics



LA LB

Player Role



Y<=I-‐mot goal


Learner You-‐player role


influence the de

sign of



Behavior

Needs Satisfaction

Game elements

Game mechanics



LA LB

Player Role



Y<=I-‐mot goal




I-‐mot goal I-‐mot goal

*

influence the de

sign of

I-‐gameplay: Gameplay Strategy


Gameplay

Behavior

Needs Satisfaction

Game elements

Game mechanics



Player Role


LA LB

The gameplay strategy is the ra@onal arrangement among player roles, mo7va7onal strategies and game mechanics.

I-‐gameplay

P-‐Player Role Primary focus

1

Y<=I-‐mot goal S<=P-‐mot goal 1

Game mechanics What use *

I-‐gameplay: Gameplay Strategy


Gameplay

Behavior

Needs Satisfaction

Game elements

Game mechanics



Player Role


LA LB

The gameplay strategy is the ra@onal arrangement among player roles, mo7va7onal strategies and game mechanics.

I-‐gameplay

P-‐Player Role Primary focus

1

Y<=I-‐mot goal S<=P-‐mot goal 1

Game mechanics What use *

Models of Player Types

classify & decide



CL Scenario

Y<=I-‐goal

W(A)-‐goal

Learning Strategy *

CL process *

Y<=I-‐goal Learning Strategy

*

Gamified CL Scenario

is a

I-‐mot goal

Y<=I-‐mot goal Mo@va@onal Strategy

*



I-‐mot goal * G

Gameplay strategy I-‐gameplay *

Game mechanics What use

*



Enhance

CL Scenario

Y<=I-‐goal

W(A)-‐goal

Learning Strategy *

CL process *


*


is a

I-‐mot goal


*



I-‐mot goal * G



*



Enhance

CL Scenario

Y<=I-‐goal

W(A)-‐goal

Learning Strategy *

CL process *


*


Player Role


*


*


*

is a

I-‐mot goal


*



I-‐mot goal * G



*



Enhance

CL Scenario

Y<=I-‐goal

W(A)-‐goal

Learning Strategy *

CL process *


*


Player Role


*


*


*

is a

I-‐mot goal


*



I-‐mot goal * G



*

the proper player types and game mechanics for each learner

Applica@on

LA LB


Applica@on of Ontological Structure

(Socializer) (Killer)

(Explorer)

Player Types (Bartle, 2004)

(Achiever)


Player Role


*


*


*

Enhance Y<=I-‐goal

Learning Strategy *


I-‐mot goal


*



I-‐mot goal * G



*


Applica@on

N1: mastery N2: relatedness N3: autonomy

LA LB

psychological needs

N2 N1

I1: system-‐orienta@on I2: ac@ng-‐orienta@on I3: interac@ng-‐orienta@on

(1) Select the scenarios that sa@sfy the learners’ needs by looking the ind. mot. goal


Applica@on


LA LB

psychological needs

N2 N1


sa7sfies

Sept. 9, 2014



An Ontology to Gamify CL Scenarios 81

Applica@on

LA LB

psychological needs

N2 N1

(2) Checking the necessary condi@ons to play a player type, and seing the priority using the desired condi@ons

Sept. 9, 2014




Applica@on

LA LB

psychological needs

N2 N1

ok

more priority

ok

ok

ind. pers. traits

I2 I1

sa7sfies

Sept. 9, 2014



(3) Seing the player type if no one constraint is violated in the mot. strategy


Applica@on

LA LB

more priority

Sept. 9, 2014




Applica@on

LA LB

more priority

Sept. 9, 2014



(4) Seing the proper game mechanics using the structure gameplay


Applica@on

LA LB

more priority




Applica@on

LA LB

psychological needs

N3 N1




Applica@on

LA LB

ok

more priority

ok

ok

ind. pers. traits

I3 I1

psychological needs

N3 N1




Applica@on

LA LB

more priority

Conclusions and Future Research


•  We proposed an ontology engineering approach to overcome the difficul@es to apply gamifica@on in CL scenarios;

•  We made the connec@on among psychological needs, playing styles and game mechanics to define our ontology;

•  We shown an example of applica@on using the current version of our approach;

Gameplay

Behavior

Needs Satisfaction

Game elements

Game mechanics



Player Role


Conclusions and Future Research


•  We proposed an ontology engineering approach to overcome the difficul@es to apply gamifica@on in CL scenarios;

•  We made the connec@on among psychological needs, playing styles and game mechanics to define our ontology;

•  We shown an example of applica@on using the current version of our approach;

Gameplay

Behavior

Needs Satisfaction

Game elements

Game mechanics



Player Role


Affective states

Game dynamics

Game interfaces

•  Future works: We will complete the modeling of Gamified CL Scenarios, including:

•  Design of game dynamics •  Design of game interfaces •  Design of game models •  …

•  Challco, G. C., Moreira, D., Mizoguchi, R., & Isotani, S. (2014, January). Towards an Ontology for Gamifying Collabora@ve Learning Scenarios. In Intelligent Tutoring Systems (pp. 404-‐409). Springer Interna@onal Publishing.

•  Schell, J. (2008). The Art of Game Design: A book of lenses. CRC Press. •  Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011, September). From game

design elements to gamefulness: defining gamifica@on. In Proceedings of the 15th Interna5onal Academic MindTrek Conference: Envisioning Future Media Environments (pp. 9-‐15). ACM.

•  Webb, E. N. (2013). Gamifica@on: When It Works, When It Doesn’t. In Design, User Experience, and Usability. Health, Learning, Playing, Cultural, and Cross-‐Cultural User Experience (pp. 608–614). Springer.

•  Hamari, J., Koivisto, J., & Sarsa, H. (2014, January). Does Gamifica@on Work? -‐ A Literature Review of Empirical Studies on Gamifica@on. In System Sciences (HICSS), 2014 47th Hawaii Interna5onal Conference on (pp. 3025-‐3034). IEEE.

•  Isotani, S., & Mizoguchi, R. (2007). Deployment of ontologies for an effec@ve design of collabora@ve learning scenarios. In Groupware: Design, Implementa@on, and Use (pp. 223–238). Springer.

References


(Istoani et al. 2009)

•  Isotani, S., Inaba, A., Ikeda, M., & Mizoguchi, R. (2009). An ontology engineering approach to the realiza@on of theory-‐driven group forma@on. Interna@onal Journal of Computer-‐Supported Collabora@ve Learning, 4(4), 445–478.

•  Isotani, S., Mizoguchi, R., Isotani, S., Capeli, O. M., Isotani, N., De Albuquerque, A. R., ... & Jaques, P. (2013). A Seman@c Web-‐based authoring tool to facilitate the planning of collabora@ve learning scenarios compliant with learning theories. Computers & Educa5on, 63, 267-‐284.

•  Pritchard, R., & Ashwood, E. (2008). Managing mo5va5on: A manager's guide to diagnosing and improving mo5va5on. Psychology Press.

•  Deci, E. L., & Ryan, R. M. (2010). Self-‐Determina5on. John Wiley & Sons, Inc.. •  Pink, D. H. (2011). Drive: The surprising truth about what mo5vates us. Penguin.. •  Laws, R. D. (2002). Robin's Laws of good game mastering. Steve Jackson Games. •  Bartle, R. A. (2004). Designing virtual worlds. New Riders •  Marczewski, A. (2013). Gamifica5on: a simple introduc5on. Andrzej Marczewski.

References




References


Logo 3

Ontological Structure for CL Scenarios


CL Scenario

Y<=I-‐goal

I-‐goal

W(A)-‐goal

Learning Strategy *

Learner I-‐role

Learner You-‐role

I-‐goal

CL process *

G


*


*

I-Role (Role Concept)

Learning Strategy (Context)

depend on

Learner (Potential Player)

playing

I-Role-1 Geiser (Role-playing thing)

Learning by Teaching Tutor

(Role-Holder) Tutor-1

The model of roles (Mizoguchi et al., 2007) proposes the division of Role in: •  Role Concept •  Poten@al Player •  Role-‐playing thing •  Role Holder

I-‐mot goal: Internaliza@on of mo@va@on


Behavior

Needs Satisfaction

Game elements

Game mechanics


Self-‐determina@on Theory (Deci & Ryan, 2010) … from … to

LA LB

I-‐mot goal

Internaliza@on of mo@va@on Ini@al stage

Goal stage Mo@va@on stage

Mo@va@on stage

is a is a

Sa@sfac@on of need

*

I-‐mot goal: Sa@sfac@on of needs


Behavior

Needs Satisfaction

Game elements

Game mechanics



LA LB

I-‐mot goal


is a is a

*

Ini@al stage

without need

Psychological need Goal stage

Sa@sfac@on of need

Purpose

Autonomy Mastery

Competence Relatedness Autonomy

Pink Dan (2011)



Y<=I-‐goal

I-‐mot goal

Y<=I-‐mot goal

Learning Strategy *

Mo@va@on Strategy *



I-‐mot goal * G

Applica@on




*

Enhance


LA LB

(2) checking the necessary condi@ons to play the player roles and seing the priority of scenarios employing the desired condi@ons

psychological needs

N2 N1

Can play

ind. pers. traits

I2 I1

Set priority

I1: interact-‐orienta@on I2: user-‐orienta@on I3: system-‐orienta@on

Can play

Set priority

psychological needs ind. pers. traits

I3 I1 N3 N1



Y<=I-‐goal

I-‐mot goal

Y<=I-‐mot goal

Learning Strategy *

Mo@va@on Strategy *



I-‐mot goal * G

Applica@on

S1: Scenario for socializa@on S2: Scenario for achievement S3: Scenario for explora@on



*

Enhance


LA LB

(3) checking the constraint for all learners, and seing the player type if no one constraint is violated in the selected scenarios

Can play

Set player role S1 à S2

S3 à S2

priority stack for LA

priority stack for LB

Selected scenarios


more priority

less priority more priority less priority

ok ok ok

ok ok ok ok

ok

ok

L(A)

L(A) L(B) L(B)

L(B) can’t play the socializer role



Y<=I-‐goal

I-‐mot goal

Y<=I-‐mot goal

Learning Strategy *

Mo@va@on Strategy *



I-‐mot goal * G

Applica@on

S1: Scenario for socializa@on S2: Scenario for achievement S3: Scenario for explora@on



*

Enhance


LA LB

S1 à S2

S3 à S2

priority stack for LA

priority stack for LB

achiever

explorer

Se@ng

(4) seing the proper game mechanics for all learners using the selected scenarios and player types

•  Mizoguchi, R., Sunagawa, E., Kozaki, K., and Kitamura, Y. (2007). The model of roles within an ontology development tool: Hozo. Applied Ontology, 2(2):159– 179.

References



an ontology engineering approach to gamify collaborative learning scenarios

Science

problems games

scenarios approach

scenarios applica

feeling successful games

ontology engineering

playful elements

use of game design elements

game contexts deterding