rosella gennari- intelligent systems and learning centred design

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LEARNER CENTRED DESIGN INTELLIGENT SYSTEMS and Rosella Gennari http://www.inf.unibz.it/~gennari Distinguished Speakers Oxford Women in CS

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Page 1: Rosella Gennari- Intelligent systems and learning centred design

LEARNER CENTRED DESIGN

INTELLIGENT SYSTEMS

and

Rosella Gennari

http://www.inf.unibz.it/~gennari

Distinguished Speakers

Oxford Women in CS

Page 2: Rosella Gennari- Intelligent systems and learning centred design

Settingtechnology

enhanced learning

Climax

TERENCE case

study

Resolutionreflections

Story outline

Page 3: Rosella Gennari- Intelligent systems and learning centred design

Settingtechnology

enhanced learning

Climax

TERENCE case

study

Resolutionreflections

Story outline

Page 4: Rosella Gennari- Intelligent systems and learning centred design

TECHNOLOGY ENHANCED LEARNING

Technology Enhanced Learning

(TEL) is the usage of technology

for supporting a learning

experience

Herby we take a narrow view:

intelligent TEL =

Artificial Intelligence (AI)

technology based products

for supporting a learning

experience

Page 5: Rosella Gennari- Intelligent systems and learning centred design

TEL 4 LEARNING EXPERIENCE

How can we design

technological products

that supports their users’

learning experience?

Page 6: Rosella Gennari- Intelligent systems and learning centred design

LET'S SEE EDUCATORS' VIEWPOINT...

Maria Montessori (1870-1952)

Paraphrasing her words, adequate

tasks that come in a prepared

environment, designed on top of the

learner characteristics can

effectively support the learner’s

learning

was the first Italian woman physician

and educator, best known for

Montessori pedagogy

Page 7: Rosella Gennari- Intelligent systems and learning centred design

ousability of technology learning products

opedagogical effectiveness of technology learning products

TEL 4 LEARNING EXPERIENCE

Adequate tasks that come in a prepared

environment designed on top of the learner

characteristics can effectively support learning

Page 8: Rosella Gennari- Intelligent systems and learning centred design

HOW TO DESIGN USABLE AND PEDAGOGICALLY EFFECTIVE TEL

Page 9: Rosella Gennari- Intelligent systems and learning centred design

Based on UCD process diagram (© Tom Wellings)

requirement

specification

designevaluation

plan

models +

prototypes

intermediate

product

final

product

HOW TO DESIGN USABLE AND PEDAGOGICALLY EFFECTIVE TEL

USABILITY + P. EFFECTIVENESS

Page 10: Rosella Gennari- Intelligent systems and learning centred design

Settingtechnology

enhanced learning

Climax

TERENCE case

study

Resolutionreflections

Story outline

Page 11: Rosella Gennari- Intelligent systems and learning centred design

TERENCE DESIGN

Based on UCD process diagram (© Tom Wellings)

requirement

specification

designevaluation

plan

models +

prototypes

intermediate

products

final

products

TERENCE was an FP7 TEL project

blending user centred and evidence based design

USABILITY + P. EFFECTIVENESS

Page 12: Rosella Gennari- Intelligent systems and learning centred design

THE PROBLEM

‣ TERENCE developed an adaptive learning system (ALS) that, via a learner GUI, recommends poor comprehenders

- its learning material, i.e., books of stories and games

- its learning tasks, i.e., reading and playing

‣ so as to stimulate their reading comprehension

‣More than 10% of primary school children, older than 8, are diagnosed with deep text comprehension problems

‣ They are referred to as poor comprehenders

Page 13: Rosella Gennari- Intelligent systems and learning centred design

THE TERENCE WORLD

a d e q u a t e

b o o k o f

s t o r i e s

s i g n i n

a d e q u a t e

s m a r t

g a m e s

r e w a r d

Page 14: Rosella Gennari- Intelligent systems and learning centred design

TERENCE INTELLIGENT TEL PRODUCTS

ALS LayerGUI Layer

Learner

EducatorExpert

Learner GUI

Expert GUI

Persistence Layer

OpenRDF

UserManager

OpenRDF

StoryManager

OpenRDF

GameManager

OpenRDF

VisualisationManager

illustrations

NPL

Reasoner

AdaptiveEngine

Visualisation

������������

Reasoning

Module

Annotation

Module

Visualisation

Module

game

generation

adaptation to

learners

Page 15: Rosella Gennari- Intelligent systems and learning centred design

Requirements Prototypes Analytic + small ev. Int. prod. Analytic+large Fin. prod.

TERENCE DESIGN

Page 16: Rosella Gennari- Intelligent systems and learning centred design

TERENCE DESIGN

Requirements Prototypes Analytic + small ev. Int. prod. Analytic+large Fin. prod.

Page 17: Rosella Gennari- Intelligent systems and learning centred design

DATA GATHERING METHODS

‣Data for designing the learning material and tasks were from

- contextual inquiries with

‣ IT & UK diagnosis

‣ as well as IT & USA evidence-based medicine therapy experts

- field studies with educators and primary school children

4510

~ 500

Page 18: Rosella Gennari- Intelligent systems and learning centred design

C

H

A

R

A

C

T

E

R

I

S

T

I

C

S

Persona Name: Carol

Age: 8

Classroom: year 4

RC levels: low reading levels

Rural/Urban: urban

Deaf/hearing: deaf

First Language: Italian Sign Language

Cochlear Implantation (if deaf): yes

Degree of hearing loss (if deaf): profound

Motor skills (if deaf): average

Summary of the class

represented by this

persona

A younger deaf girl who is very enthusiastic about using new

technology (such as iPhone and IPad) and who adores her

Nintendo DS. Her reading RC levels are very low, but she reads

together with her parents to learn new words and spelling. She

also likes to do many other things such as drawing, taking care of

her pets and going to the park.

Quote “I really love Mario and Luigi. And I would love to have an iPhone

and an IPad, like my dad.”

Personality Open

Role in classroom Active

Role out of the

classroom

Active

Console/Technology Carol and her sister watch TV after school. They like Tom & Jerry,

Ben 10, Hello Kitty and Mickey Mouse Clubhouse.

Carol sometimes uses the computer, but only to play minilab

games. Her computer is in her bedroom, but her parents don’t

allow her to use it all of the time. She can use it only one hour per

day. Carol’s dad has an iPhone and an IPad, and Carol would

really like to use those as well, but her dad tells her she is a bit

too young. Carol likes watching him with his IPad and iPhone

though.

Carol doesn’t use a mobile phone.

Carol plays games on the computer and on her Nintendo DS.

She plays by herself. She likes the mini-clip games on the

computer, and Mario Kart and brain training games on her DS.

She likes games with non-photorealistic human avatars, and

prefers fantasy avatars to animal avatars.

Socio-Cultural Level of

his/her own family

Medium

School performance Carol has sever reading problems. In her class she is below

average in all activities but drawing, where she feels she can truly

express her intimate feelings.

Homework After school, Carol does her homework together with her mum.

L

I

F

E

S

T

Y

L

E

Outdoors Activities Carol often goes to the park with her mum.

Indoors Activities Games on the DS

Carol reads sometimes. Her mum and dad help her reading in the evening. She likes some of the stories they read together, but mostly, she wants to read because she has to learn new words and spelling.

Carol likes drawing and taking care of her pets.

Her mum often plays with her.

Home activities Carol also likes to help her mum in the kitchen or in the garden.

Sport activities Carol practices no specific sport.

.

Page 19: Rosella Gennari- Intelligent systems and learning centred design

SMART GAME REQUIREMENTS

What for Description

Difficulty levels Macro levels for learners: - entry: character games; - intermediate: time games; - top: causality games.

Scheduling of reading and playing

1st silent reading; 2nd playing smart games; 3rd playing relaxing games

Constraints on actions Learners should get faster, hence a game has a maximal resolution time

Progress and feedback Monitor and give learners (1) visible idea of progress, (2) explanatory feedback, (3) recall their attention and solicit them to give a resolution (in time)

Representation Production can be impaired hence promote resolution via visual representation and reasoning

Page 20: Rosella Gennari- Intelligent systems and learning centred design

Requirements Prototypes Analytic + small ev. Int. prod. Analytic+large Fin. prod.

TERENCE DESIGN

Page 21: Rosella Gennari- Intelligent systems and learning centred design

SMART GAME REQUIREMENTS

What for Description

Difficulty levels Macro levels for learners: - entry: character games; - intermediate: time games; - top: causality games.

Scheduling of reading and playing

1st silent reading; 2nd playing smart games; 3rd playing relaxing games

Constraints on actions Learners should get faster, hence a game has a maximal resolution time

Progress and feedback Monitor and give learners (1) visible idea of progress, (2) explanatory feedback, (3) recall their attention and solicit them to give a resolution (in time)

Representation Production can be impaired hence promote resolution via visual representation and reasoning

Page 22: Rosella Gennari- Intelligent systems and learning centred design

who is the actor of … ? what does (a main) character do?

when does … happen in relation to a central

event?why does the central event happen?

Page 23: Rosella Gennari- Intelligent systems and learning centred design

SMART GAME REQUIREMENTS

What for Description

Difficulty levels Macro levels for learners: - entry: character games; - intermediate: time games; - top: causality games.

Scheduling of reading and playing

1st silent reading; 2nd playing smart games; 3rd playing relaxing games

Constraints on actions Learners should get faster, hence a game has a maximal resolution time

Progress and feedback Monitor and give learners (1) visible idea of progress, (2) explanatory feedback, (3) recall their attention and solicit them to give a resolution (in time)

Representation Production can be impaired hence promote resolution via visual representation and reasoning

Page 24: Rosella Gennari- Intelligent systems and learning centred design

points for each smart

coins for all smartunlocked if read+play

visual feedback

Page 25: Rosella Gennari- Intelligent systems and learning centred design

Instructions Questions Motivational Interaction

Choices Choices for learner Fixed event

Solutions Choices that are either correct (c) or wrong (w)

Feedback Interaction Consistency Explanatory Solution

Smart points Proportional to the learner’s ability in the game level

Relaxing points

Constant

Avatar Happy/sad states

Time solution constant interaction constant

Rules States of the system, actions of the learner, constraints

What for Description

Difficulty levels Macro levels for learners: - entry: character games; - intermediate: time games; - top: causality games.

Scheduling of reading and playing

1st silent reading; 2nd playing smart games; 3rd playing relaxing games

Constraints on actions

Learners should get faster, hence a game has a maximal resolution time

Progress and feedback

Monitor and give learners (1) idea of progress, (2) explanatory feedback, (3) recall their attention and solicit them to give a resolution (in time)

Representation Production can be impaired hence promote resolution via visual representation and reasoning

Page 26: Rosella Gennari- Intelligent systems and learning centred design

Requirements Prototypes Analytic + small ev. Int. prod. Analytic+large Fin. prod.

TERENCE DESIGN

Page 27: Rosella Gennari- Intelligent systems and learning centred design

EXAMPLE METHODS IN TERENCE

APPROACH WITH WHOM EXAMPLE METHODS WHEN

analyticalHMI experts or domain experts

heuristic evaluationformative, summative

expert evaluation

cognitive walk-through

small-scale learnersobservations

formativethink aloud

large-scale learners field studies summative

Page 28: Rosella Gennari- Intelligent systems and learning centred design

EXAMPLE METHODS IN TERENCE

APPROACH WITH WHOM EXAMPLE METHODS HOW

analyticalHMI experts or domain experts

heuristic evaluationformative, summative

expert evaluation

cognitive walk-through

small-scale learnersobservations

formativethink aloud

large-scale learners field studies summative

Page 29: Rosella Gennari- Intelligent systems and learning centred design

G1. interfaces follow general design guidelines

G2. interfaces support the user’s next step to achieve a task

G3. interfaces provide users with timely feedback

Instructions are not under focus and cannot be easily read

Game question and possible resolutions should be proximally close

Game question and possible resolutions should be proximally close

Evaluation of interfaces

Page 30: Rosella Gennari- Intelligent systems and learning centred design

Requirements Prototypes Analytic + small ev. Int. prod. Analytic+large Fin. prod.

TERENCE DESIGN

Page 31: Rosella Gennari- Intelligent systems and learning centred design

problem: 256 stories,

each with ~12 games

Smart game design

Page 32: Rosella Gennari- Intelligent systems and learning centred design

how can we automatise the

development of smart games via AI (and, hopefully, be efficient)?

Smart game design

Page 33: Rosella Gennari- Intelligent systems and learning centred design

enriched annotations

story

annotations

ALS LayerGUI Layer

Learner

EducatorExpert

Learner GUI

Expert GUI

Persistence Layer

OpenRDF

UserManager

OpenRDF

StoryManager

OpenRDF

GameManager

OpenRDF

VisualisationManager

illustrations

NPL

Reasoner

AdaptiveEngine

Visualisation

������������

Reasoning

Module

Annotation

Module

Visualisation

Module

Semi-automated generation

Page 34: Rosella Gennari- Intelligent systems and learning centred design

enriched annotations

story

text

text

text

text

annotations

Semi-automated generation

ALS LayerGUI Layer

Learner

EducatorExpert

Learner GUI

Expert GUI

Persistence Layer

OpenRDF

UserManager

OpenRDF

StoryManager

OpenRDF

GameManager

OpenRDF

VisualisationManager

illustrations

NPL

Reasoner

AdaptiveEngine

Visualisation

������������

Reasoning

Module

Annotation

Module

Visualisation

Module

Page 35: Rosella Gennari- Intelligent systems and learning centred design

text

text

text

textimage

image image image

enriched annotations

story

annotations

Semi-automated generation

Page 36: Rosella Gennari- Intelligent systems and learning centred design

games

template visual

text

text

text

textimage

image image image

enriched annotations

story

annotations

Semi-automated generation

ALS LayerGUI Layer

Learner

EducatorExpert

Learner GUI

Expert GUI

Persistence Layer

OpenRDF

UserManager

OpenRDF

StoryManager

OpenRDF

GameManager

OpenRDF

VisualisationManager

illustrations

NPL

Reasoner

AdaptiveEngine

Visualisation

������������

Reasoning

Module

Annotation

Module

Visualisation

Module

Page 37: Rosella Gennari- Intelligent systems and learning centred design

text

story

text + visual

games

AUTOM. MANUAL AUTOM.

Semi-automated generation

Page 38: Rosella Gennari- Intelligent systems and learning centred design

Requirements Prototypes Analytic + small ev. Int. prod. Analytic+large Fin. prod.

TERENCE DESIGN

Page 39: Rosella Gennari- Intelligent systems and learning centred design

APPROACH WITH WHOM EXAMPLE METHODS WHEN

analyticalHMI experts or domain experts

heuristic evaluationformative, summative

expert evaluation

cognitive walk-through

small-scale learnersobservations

formativethink aloud

large-scale learners field studies summative

EXAMPLE METHODS IN TERENCE

Page 40: Rosella Gennari- Intelligent systems and learning centred design

APPROACH WITH WHOM EXAMPLE METHODS WHEN

analyticalHMI experts or domain experts

heuristic evaluationformative, summative

expert evaluation

cognitive walk-through

small-scale learnersobservations

formativethink aloud

large-scale learners field studies summative

EXAMPLE METHODS IN TERENCE

Page 41: Rosella Gennari- Intelligent systems and learning centred design

LARGE-SCALE STUDY DESIGN

Common design of the intervention with TERENCE:

‣ how: pretest/posttest design, with experimental and control groups

‣ hypothesis: TERENCE improves reading comprehension measured

with standardized text comprehension tests

ControlExperimental

Page 42: Rosella Gennari- Intelligent systems and learning centred design

A 3-PHASE INTERVENTION

‣ Pre-test for (1) assessing txt comprehension, (2) initialising TERENCE

Page 43: Rosella Gennari- Intelligent systems and learning centred design

A 3-PHASE INTERVENTION

‣ Pre-test for (1) assessing txt comprehension, (2) initialising TERENCE

‣ Stimulation phase for experimental group with usage sessions so that each

-lasts < 45 minutes for attention needs

Page 44: Rosella Gennari- Intelligent systems and learning centred design

A 3-PHASE INTERVENTION

‣ Pre-test for (1) assessing txt comprehension, (2) initialising TERENCE

‣ Stimulation phase for experimental group with usage sessions so that each

-lasts < 45 minutes for attention needs

-and requires (1) reading

Page 45: Rosella Gennari- Intelligent systems and learning centred design

A 3-PHASE INTERVENTION

‣ Pre-test for (1) assessing txt comprehension, (2) initialising TERENCE

‣ Stimulation phase for experimental group with usage sessions so that each

-lasts < 45 minutes for attention needs

-and requires (1) reading (2) playing smart

Page 46: Rosella Gennari- Intelligent systems and learning centred design

‣ Pre-test for (1) assessing txt comprehension, (2) initialising TERENCE

‣ Stimulation phase for experimental group with usage sessions so that each

-lasts < 45 minutes for attention needs

-and requires (1) reading (3) playing relaxing(2) playing smart

A 3-PHASE INTERVENTION

Page 47: Rosella Gennari- Intelligent systems and learning centred design

‣ Post-test (pedagogical only) for re-assessing text comprehension

‣ Pre-test for (1) assessing txt comprehension, (2) initialising TERENCE

‣ Stimulation phase for experimental group with usage sessions so that each

-lasts < 45 minutes for attention needs

-and requires (1) reading (3) playing relaxing(2) playing smart

A 3-PHASE INTERVENTION

Page 48: Rosella Gennari- Intelligent systems and learning centred design

The experimental group is of 344 learners:

‣Avezzano: 270 learners:

- 7-9 years old: 118

- 9-11 years old: 152

‣ Pescina: 74 learners:

- 7-9 years old: 37

- 9-11 years old: 37

‣ They were tested (January-February), stimulated (March-May), and re-tested (may-June)

EXPERIMENTAL GROUP IN IT

Page 49: Rosella Gennari- Intelligent systems and learning centred design

Pre-post performances for text comprehension (dependent variable) were as follows:

‣ Pescina:

- pre: 14 poor comprehenders (20.59%)

- post: 6 poor comprehenders (8.82%)

‣ Avezzano:

- pre: 15 poor comprehenders (5.95%)

- post: 2 poor comprehenders (0.79%)

MAIN RESULTS IN IT

Pre

Pescina Avezzano

5,95%

20,59%

‣ Wilcoxon signed-rank test supports that differences are statistically significant

- Pescina: z=-4.904, p<0.0001

- Avezzano: z=-2.266, p=0.0234

Page 50: Rosella Gennari- Intelligent systems and learning centred design

EXAMPLE METHODS IN TERENCE

APPROACH WITH WHOM EXAMPLE METHODS HOW

analyticalHMI experts or domain experts

heuristic evaluationformative, summative

expert evaluation

cognitive walk-through

small-scale learnersobservations

formativethink aloud

large-scale learners field studies summative

Page 51: Rosella Gennari- Intelligent systems and learning centred design

EXPERT EVALUATION

Experts of pedagogy: 1 coordinator; 9 evaluators

Sophie'comes'down'the'steps

He had never been beaten before, since he

only ever raced with kids who were

smaller and slower than him.

He wanted a rematch, so the two boys set

off again. Ben was paddling as fast as he

could, still he didn’t make it to the wall

before Luke. It was completely unfair, he

thought. Luke was so much faster. No

sooner had they climbed out of the water,

than he saw his sister coming down the

steps. She was smiling at Ben and gave

him a playful pat on the shoulder. She also

gave Ben a friendly speech about winners

and losers.

revise selection of

solutions

revise selection of

central event

How-to:

1. each pair of evaluators read a story, and edited its games

2. the coordinator revised their work

3. a pair of evaluators was blindly assigned revised games, and another the manually created games

Main edit tasks:

(1) creation of missing games (~recall)

(2) revision of games (~precision)

Page 52: Rosella Gennari- Intelligent systems and learning centred design

From D4.2 and D4.3 technical annex

overall assessment of generation

text

story

text + visual

games

revision of Automated Reasoning (AR) selection of central events and solutions

revision of Natural Language Processing (NLP) of text

text

text text text

Edit tasks in details

Page 53: Rosella Gennari- Intelligent systems and learning centred design

Requirements Prototypes Analytic + small ev. Int. prod. Analytic+large Fin. prod.

TERENCE DESIGN

Page 54: Rosella Gennari- Intelligent systems and learning centred design

From D4.2 and D4.3 technical annex

overall generation

text

story

text + visual

games

AR selection of central events and solutions

revision of NLP text

text

text text text

Analyses of evaluation results

Page 55: Rosella Gennari- Intelligent systems and learning centred design

AR selection of central events for games:

>Results: only in 15 out of 250 cases (6%), it was necessary to select a different central event than the automatically generated one

From D4.2 and D4.3 technical annex

>Implications for AR: none picked up

Automated part evaluation-based re-design

Page 56: Rosella Gennari- Intelligent systems and learning centred design

AR selection of plausible solutions:

>Results: out of 140 changes of selection of solutions, the majority was for wrong solutions

- generate a wrong solution from correct one by changing participants, e.g.,

<correct_sentence id="2">The man ran and fell on the ground.

</correct_sentence>

<wrong_sentence id="2wh1">Peter ran and fell on the ground.

</wrong_sentence>

>Implications for WP4: new heuristics for wrong plausible solutions in the last part of Y3,

From D4.2 and D4.3 technical annex

Automated part evaluation-based re-design

Page 57: Rosella Gennari- Intelligent systems and learning centred design

Overall generation: development times:

>Results for revision time:

- 12’6” per game instance:

↑ 12’8” for time games

↓ 10’6” for who games

>Results for creation time:

- avg. 23” per game instance

text

story

text + visual

games

From D4.2 and D4.3 technical annex

>Implications for AR: the semi-automated development process seems to be promising for optimising development times

Automated part evaluation-based re-design

Page 58: Rosella Gennari- Intelligent systems and learning centred design

Game over

1st 2nd 3

Sep. 2011 December 2012 September 2013

Sophie'comes'down'the'steps

He had never been beaten before, since he

only ever raced with kids who were

smaller and slower than him.

He wanted a rematch, so the two boys set

off again. Ben was paddling as fast as he

could, still he didn’t make it to the wall

before Luke. It was completely unfair, he

thought. Luke was so much faster. No

sooner had they climbed out of the water,

than he saw his sister coming down the

steps. She was smiling at Ben and gave

him a playful pat on the shoulder. She also

gave Ben a friendly speech about winners

and losers.

revise selection of

solutions

revise selection of

central event

Requirements+for Description

Dif$iculty*levels Macro*levels*for*learners:

4*entry:*character*games;

4*intermediate:*time*games;

4*top:*causality*games.*Scheduling*of*

reading*and*playing

1st*silent*reading;* 2nd* playing* smart* games;*3rd*playing*

relaxing*games

Constraints*on*

actions

Learners* should* get* faster,* hence* a* game* has* a* maximal*

resolution+time

Progress*and*

feedback

Monitor* and* give* learners* (1)* idea* of* progress,* (2)*

explanatory*feedback,*(3)*recall*their*attention*and*solicit+

them*to*give*a*resolution*(in*time)Representation Production*can*be* impaired* hence*promote* resolution*via*

visual*representation+and+reasoning

Instruc(ons Ques%onsQues%ons Mo%va%onalMo%va%onalMo%va%onalMo%va%onal Interac%onInterac%on

Choices Choices3for3learnerChoices3for3learnerChoices3for3learnerChoices3for3learnerChoices3for3learner 3Fixed3event3Fixed3event3Fixed3event

Solu(ons Choices3or3their3combina%ons3that3are3correct/wrong3(c/w)3solu%onsChoices3or3their3combina%ons3that3are3correct/wrong3(c/w)3solu%onsChoices3or3their3combina%ons3that3are3correct/wrong3(c/w)3solu%onsChoices3or3their3combina%ons3that3are3correct/wrong3(c/w)3solu%onsChoices3or3their3combina%ons3that3are3correct/wrong3(c/w)3solu%onsChoices3or3their3combina%ons3that3are3correct/wrong3(c/w)3solu%onsChoices3or3their3combina%ons3that3are3correct/wrong3(c/w)3solu%onsChoices3or3their3combina%ons3that3are3correct/wrong3(c/w)3solu%ons

Feedback Interac%on Consistency3(c/w)Consistency3(c/w)Consistency3(c/w) ExplanatoryExplanatoryExplanatory Solu%on

Smart6points Propor%onal3to3the3learner’s3ability3in3the3game3levelPropor%onal3to3the3learner’s3ability3in3the3game3levelPropor%onal3to3the3learner’s3ability3in3the3game3levelPropor%onal3to3the3learner’s3ability3in3the3game3levelPropor%onal3to3the3learner’s3ability3in3the3game3levelPropor%onal3to3the3learner’s3ability3in3the3game3levelPropor%onal3to3the3learner’s3ability3in3the3game3levelPropor%onal3to3the3learner’s3ability3in3the3game3level

Relaxing6points ConstantConstantConstantConstantConstantConstantConstantConstant

Avatar Happy/sad3statesHappy/sad3statesHappy/sad3statesHappy/sad3statesHappy/sad3statesHappy/sad3statesHappy/sad3statesHappy/sad3states

Time solu%on3constantsolu%on3constantsolu%on3constant interac%on3constantinterac%on3constantinterac%on3constantinterac%on3constantinterac%on3constant

Rules States3of3the3system,3ac'ons3of3the3learner,3constraintsStates3of3the3system,3ac'ons3of3the3learner,3constraintsStates3of3the3system,3ac'ons3of3the3learner,3constraintsStates3of3the3system,3ac'ons3of3the3learner,3constraintsStates3of3the3system,3ac'ons3of3the3learner,3constraintsStates3of3the3system,3ac'ons3of3the3learner,3constraintsStates3of3the3system,3ac'ons3of3the3learner,3constraintsStates3of3the3system,3ac'ons3of3the3learner,3constraints

data structures

NLP+ AR1 for stories

AR1 for txt games

frameworkAR2 + NLP1 for txt games

AR2 for stories AR3 + NLP2 for txt games

requirements

Page 59: Rosella Gennari- Intelligent systems and learning centred design

Settingtechnology

enhanced learning

Climax

TERENCE case

study

Resolutionreflections

Story outline

Page 60: Rosella Gennari- Intelligent systems and learning centred design

Till 2007

Cat

egory

Axi

s

AR

HMI

TEL

Game

0 4 8 12 16

Response: work areas

Amsterdam U. and CWI

FBK-irst

Free U. of Bolzano

Page 61: Rosella Gennari- Intelligent systems and learning centred design

From 2007

Cat

egory

Axi

s

AR

HMI

TEL

Game

0 4 8 12 16

Response: work areas

Free U. of Bolzano

Page 62: Rosella Gennari- Intelligent systems and learning centred design

Possible explanation?

Page 63: Rosella Gennari- Intelligent systems and learning centred design

co-designgamification cooperative learning

HOW

WHYengagement design together inclusion

childrendesigners teachersWHO

WHAT

GACOCO

treestree puzzle

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Gamification of protocol (tasks, subtasks and types of feedback)

Page 65: Rosella Gennari- Intelligent systems and learning centred design

Gamification (competition for cooperation)

MISSIONS

CHALLENGES

REWARDSwell

done!

Page 66: Rosella Gennari- Intelligent systems and learning centred design

Acknowledgments to

TERENCE colleagues and schools

Current colleagues and schools

DIARY FOR PRESENT

THE TERENCE BOOK

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Settingtechnology

enhanced learning

Climax

TERENCE case

study

Resolutionreflections

Story outline

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