from research to innovation in computers and education: bridging the gap between science and social...
TRANSCRIPT
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1April 20, 2016
From Research to Innovation in Computers and Education: Bridging the Gap between Science and Social Contribution
University of Saskatchewan, Saskatoon, Canada
Center of Excellence for Social TechnologiesComputing Institute (IC) - Federal University of Alagoas (UFAL)
[email protected] Ibert Bittencourt
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Brasil
April 20, 2016 2July, 2007
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Brasil
April 20, 2016 3
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April 20, 2016 4
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Maceió
April 20, 2016 5
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Maceió
Maceió, Alagoas, BrazilApril 20, 2016 6
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8April 20, 2016
From Research to Innovation in Computers and Education: Bridging the Gap between Science and Social Contribution
University of Saskatchewan, Saskatoon, Canada
Center of Excellence for Social TechnologiesComputing Institute (IC) - Federal University of Alagoas (UFAL)
[email protected] Ibert Bittencourt
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The beginning...
Research and Innovation...
Bridging the gap...
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MeuTutor
How it was created?
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Many researchers working with Computers in Education have a dream ....
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...that is:research achievements should also have a strong impact on real classrooms and teaching-learning processes
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Unfortunately, this dream does not come true to most of us !
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Usually it happens because we often choose to follow two distinct path ways
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óri
Technology-oriented Research
Cutting-edge technological advancement, tested in controlled environments. Yet, not mature enough to be used in classrooms
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óri
Pedagogy-oriented research
Understanding on how to instructional processes and
Technologies affect students and teachers. It has strong local impact,
yet, it is very difficult to be generalized and used in large scale
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Ig Ibert Bittencourt | [email protected]
19http://media.linkjab.com/i/images/university-of-bologna-the-worlds-oldest-university.jpg
April 20, 2016
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Ig Ibert Bittencourt | [email protected]
20http://media.linkjab.com/i/images/university-of-bologna-the-worlds-oldest-university.jpg
April 20, 2016 July, 2007
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2007
IG IBERTPHD UFCG
SEIJI ISOTANIPHD OSAKA/Japão
We decided to create a platform that allow us to research on advanced technology and scale results from pedagogical research
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We also believed that such a platform should live even after we left academia
IG IBERTPHD UFCG
SEIJI ISOTANIPHD OSAKA/Japão
2007
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But, how we do that?
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We generate innovation from research findings
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And create products that will have a real impact on society
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MeuTutor
Startup company that developed a platform to create Intelligent tutoring systems
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http://meututor.com.brPersonalized Learning for All
Research Spin-off Company
MeuTutor: First Generation
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Negative feedback - First Version
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Negative feedback - First Version
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Negative feedback - First Version
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Negative feedback - First Version
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Negative feedback - First Version
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Negative feedback - First Version
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http://meututor.com.brPersonalized Learning for All
Research Spin-off Company
MeuTutor: Second Generation First Version
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38April 20, 2016
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39April 20, 2016
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http://meututor.com.brPersonalized Learning for All
Research Spin-off Company
MeuTutor: Second Generation Second Version
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Student Module
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Student Module
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Student Module
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Student Module
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Teacher Module
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Coordinator Mod.
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Coordinator Mod.
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Secretary of Education Module
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Secretary of Education Module
• BANDEIRA, J. M. ; AVILA, T. ; I. BITTENCOURT, IG ; Isotani, Seiji.; et al. Dados Abertor Conectados para a Educação. JAIE, Sociedade Brasileira de Computação, 2015, v. 1, p. 47-69.
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Mobile Version
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Mobile Version
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Research Spin-off Company
How does it work?
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Learn Essay
Personalized Learning
Semantically Structured
Motivational Perspective
Intelligent Approach
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Learn Essay
Personalized Learning
Semantically Structured
Motivational Perspective
Intelligent Approach
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63
Skills
Topics
Disciplines
April 20, 2016
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68April 20, 2016
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Learn Essay
Personalized Learning
Semantically Structured
Motivational Perspective
Intelligent Approach
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Learn Essay
Personalized Learning
Semantically Structured
Motivational Perspective
Intelligent Approach
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Know
ledg
e
Resources Semanticallyconnected
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Lightweight Ontologies
User Model
ExpertModel
Domain Model
Interface
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Lightweight Ontologies
• Bittencourt, I. I.; et al. (2009). A Computational Model for Developing Semantic Web-based Educational Systems. Knowledge-Based Systems.• Bittencourt, Ig Ibert; Olavo Holanda ; Isotani, Seiji . JOINT: Java Ontology Integrated Toolkit. Expert Systems with Applications, v. 40, p. 6469-6477, 2013.
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Lightweight OntologiesDomain Model
• Bittencourt, I. I.; et al. (2009). A Computational Model for Developing Semantic Web-based Educational Systems. Knowledge-Based Systems.• Bittencourt, Ig Ibert; Olavo Holanda ; Isotani, Seiji . JOINT: Java Ontology Integrated Toolkit. Expert Systems with Applications, v. 40, p. 6469-6477, 2013.
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Lightweight Ontologies
User Model
• Bittencourt, I. I.; et al. (2009). A Computational Model for Developing Semantic Web-based Educational Systems. Knowledge-Based Systems.• Bittencourt, Ig Ibert; Olavo Holanda ; Isotani, Seiji . JOINT: Java Ontology Integrated Toolkit. Expert Systems with Applications, v. 40, p. 6469-6477, 2013.
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Lightweight Ontologies
Pedagogical Model
• Bittencourt, I. I.; et al. (2009). A Computational Model for Developing Semantic Web-based Educational Systems. Knowledge-Based Systems.• Bittencourt, Ig Ibert; Olavo Holanda ; Isotani, Seiji . JOINT: Java Ontology Integrated Toolkit. Expert Systems with Applications, v. 40, p. 6469-6477, 2013.
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Lightweight OntologiesGamification Model
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Learn Essay
Personalized Learning
Semantically Structured
Motivational Perspective
Intelligent Approach
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Learn Essay
Personalized Learning
Semantically Structured
Motivational Perspective
Intelligent Approach
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GAMIFICAÇÃO
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Results Expected
Student Model
Specialist Model
Domain Model
Interface Model
ITS Core
Player Types
Gamification
Model
Gamification
ElementsGamificatio
n Layer
ITS Gamification Platform
Acquire knowledge to built a framework to help designing Personalized Gamified ITSs.
84
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MINERAÇÃO DE DADOS
PERFIL DO ESTUDANTE
PERFIL GERAL
CRIA O PERFIL DO ESTUDANTE BASEADO NA
MÉDIA
MISSÕES PERSONALIZADASALUNOIdentifica Missões para
um determinado Aspecto
Pers
onal
ized
Miss
ions
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Pers
onal
ized
Miss
ions
Colaborative
Social Gamified
Individualized
Colaborative
Social Gamified
Individualized• PAIVA, R. ; BITTENCOURT, I. I. ; et al . Improving pedagogical recommendations by classifying students according to their interactional behavior in a gamified learning environment. In: the 30th ACM SAC 2015.
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Pers
onal
ized
Miss
ions
• PAIVA, R. ; BITTENCOURT, I. I. ; et al . Improving pedagogical recommendations by classifying students according to their interactional behavior in a gamified learning environment. In: the 30th ACM SAC 2015.• PAIVA, R. ; BITTENCOURT, I. I. ; et al . What do students do online? Modeling students' interactions to improve their learning experience. EAAI. (under review)
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Peer Assessment
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640
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Peer Assessment approach to Learn Essay
• Tenório, T.; Bittencourt, I. I.; Isotani, S. A gamified peer assessment model for on-line learning environments in a competitive context. Computers and Human Behavior (under review)• Tenório, T.; Bittencourt, I. I.; Isotani, S. Does Peer Assessment in On-line Learning Environments work? A systematic review of the literature. Internet and Higher Education (under review)
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Learn Essay
Personalized Learning
Semantically Structured
Motivational Perspective
Intelligent Approach
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Learn Essay
Personalized Learning
Semantically Structured
Motivational Perspective
Intelligent Approach
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GENETIC ALGORITHMS
Adaptive Strategy (1st Generation)
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• Questions
• User Profile
Classification
• Recommendation
IRT• Micro concepts
• Learning Outcomes
BKT
Adaptive Strategy (2nd Generation)
• Ryan S. J. d. Baker, Albert T. Corbett, Vincent Aleven (2008). More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing. In. ITS Conference.
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User model updating
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User model updating
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99April 20, 2016Recommentation: Video
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Recommentation: Mission
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Peer Assessment
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Define the sample
MINE (CLUSTERING)
Recommendation Profiles
Pedagogical Recommendation
Learning Essay to ENEM ExamImprove learning
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Best Performance
Worst Performance
Define the Sample
Mine (Clustering)
RECOMMENDATION PROFILES
Pedagogical Recommendation
Group of recommendation Profiles Activities
1 1, 2 e 4 Read a new essay with grade 1000
2 3, 5, 8, 10 e 11 Resource to Competency 5
3 6, 7, 9, 12, 16, 17 e 18 Resource to Competencies 2, 3 and 5
4 14 Resource to Competencies 1 and 4
5 15 Resource to Competencies 2, 3 and 5
6 13 e 19 Resource to Competencies 1 and 4
Learning Essay to ENEM ExamImprove learning
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640
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Não houve ganho Ganho Parcial Ganho total0
2
4
6
8
10
12
14
10
13
6
Writing Two Essays
Não houve ganho Ganho Parcial Ganho total0
1
2
3
4
5
6
1
4
5
Writing Three Essays
65,5% of the students Improved 90% of the students ImprovedNo Imp. Partial Imp. Total Imp. No Imp. Partial Imp. Total Imp.
Learning Essay to ENEM ExamImprove learning
Santos, D. ; Bittencourt, I. I.; Paiva, R. Learning Essay through Educational Data Mining and Pedagogical Recomendation. Computers and Human Behavior (to be submitted)
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Learning Essay to ENEM ExamMobile Learning
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Learn Essay
Personalized Learning
Semantically Structured
Motivational Perspective
Intelligent Approach
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100 5000
+30.000
NUMBER OF USERS
STUDENTS
48%FASTER More learning
27%
April 20, 2016 108Ig Ibert Bittencourt | [email protected]
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≈ 30.000 in 2016
TWO NEW CLIENTS
109
1. Telco Company (B2C)
2. Big School Player (B2B)≈ 100.000 in 2017
≈ 20.000 in 2017≈ 50.000 in 2018
2000 10000
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Research Spin-off Company
What are the next steps?
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MeuTutor (Third Generation)
Learn Essay
Second Generation
Improvements
Collaboration
Optimal Experience
Authoring
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Second Generation
Improvements
SANTANA, S. J. ; I. BITTENCOURT, I. ; ISOTANI, S. (2016). A Quantitative Analysis of the Most Relevant Gamification Elements in an Online Learning Environment. In: WWW@WebED. Canadá: ACM, 2016. v. 1.
SANTANA, S. J. ; BITTENCOURT, I.; et al . (2016). Evaluating the impact of Mars and Venus Effect on the use of an Adaptive Learning Technology for Portuguese and Mathematics. In: IEEE ICALT.
Malta, C. ; Bittencourt, I.; et al . (2016). A Computational Model to Automatically Classify the Goal-Orientation Motivation of Students. Plos One (to be submitted)
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Collaboration
• Isotani, S. ; MIZOGUCHI, R. ; Bittencourt, I.; et al. A Semantic Web-based authoring tool to facilitate the planning of collaborative learning scenarios compliant with learning theories. Computers and Education, 2013.
• REIS, R. RODRIGUEZ, C; LYRA, Bittencourt, I. I.; Isotani, S.; et al. Affective States in CSCL: Studying the Past to Drive the Future. Computers and Education (under review).
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Collaboration
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Optimal Experience
• CHALCO, G.; ANDRADE, F. R. H. ; BORGES, S. S. ; BITTENCOURT, IG, I. ; Isotani, Seiji . Toward A Unified Modeling of Learner?s Growth Process and Flow Theory. Educational Technology & Society, 2016.• SANTOS, W. O. ; BITTENCOURT, IG, I. ; Isotani, Seiji ; MARQUES, L. ; Frango, I . Challenges of Flow Theory Applied to Computers in Education. In: DesafIE, 2015, Recife. 2015. v. 1. p. 1-10.• SANTOS, W. O. ; BITTENCOURT, IG, I. ; Isotani, Seiji ; MARQUES, L. ; Frango, I . Flow Theory applied to Computers in Education: Systematic Literature Review . Computers and Human Behavior (to be submitted).
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Authoring
Diego Dermeval
• Dermeval, Diego ; TENÓRIO, THYAGO ; BITTENCOURT, IG IBERT ; et al.. Ontology-based Feature Modeling: An Empirical Study in Changing Scenarios. Expert Systems with Applications, v. 1, p. 1-26, 2015.• Dermeval, Diego ; VILELA, JÉSSYKA ; BITTENCOURT, IG IBERT ; CASTRO, JAELSON ; Isotani, Seiji ; Brito, Patrick ; Silva, Alan . Applications of ontologies in requirements engineering: a systematic review of the
literature. Requirements Engineering (London. Print), v. 1, p. 1-35, 2015.• Dermeval, Diego ; BITTENCOURT, IG IBERT ; et al. An Ontology-driven Software Product Line Architecture for Developing Gamified Intelligent Tutoring Systems. Educational Technology & Society (under review)
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Authoring
Ranilson Paiva
• PAIVA, R. ; Bittencourt, I. I.; et al. (2014). A Systematic Approach for Providing Personalized Pedagogical Recommendations Based on Educational Data Mining. 12th ITS Conference.• Paiva. R; Bittencourt, I. I. et al. A Data-Informed Intelligent Process for the Authoring of Pedagogical Decisions. IJAIED (Under Review)
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How to bridge the gap?
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Ig Ibert Bittencourt | [email protected]
122April 20, 2016
MissionFormation of Social Leaders through RDI in ICT.
VisionIn 10 years, be an International Reference in the formation of Social Leaders
Goals
1. Generation of Scientific Knowledge2. Promotion of Social Spin-offs3. Formation of Social Leaders
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123
NEES
Artificial Intelligence in
Education
Social Entrepreneurship
Computational Ontology
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124
Research ProjectPrototype Paper PublicationProduct
4P Cycle to generate research spinoffs
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Transfer Technology Process
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Ig Ibert Bittencourt | [email protected]
“My grade in ENEM (National Exam) was 675, I was accepted in 1° Place in Law School at Federal University of Tocantins and I was accepted at Federal University of Rondônia. I used MeuTutor and it helped me a lot, mainly in Math, because I have lots of difficult”
“I did ENEM (National Exam) and had 127 correct questions from 180. I was approved in Medicine at UMC. I studied alone and the MeuTutor-Platform was very important because of the videos and explicative questions. Congratulations for the Platform”
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Social Spinoffs
Social Inequality
Research
Innovation
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Ig Ibert Bittencourt | [email protected]
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Ig Ibert Bittencourt | [email protected]
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Maceió
Maceió, Alagoas, Brazil
Thank you!Muito Obrigado!
Merci Beaucoup!April 20, 2016 131
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132April 20, 2016
From Research to Innovation in Computers and Education: Bridging the Gap between Science and Social Contribution
University of Saskatchewan, Saskatoon, Canada
Center of Excellence for Social TechnologiesComputing Institute (IC) - Federal University of Alagoas (UFAL)
[email protected] Ibert Bittencourt