SlideShare a Scribd company logo
1 of 21
Download to read offline
Roger Azevedo, Ph.D.
University of Central Florida
School of Modeling, Simulation, and Training
Learning Sciences Faculty Cluster Initiative
Departments of Computer Science and Internal Medicine
Laboratory for the Study of Metacognition and Advanced Learning Technologies
Accelerating Self-Regulated Learning with AI:
Opportunities and Challenges
• Science of learning with technology and multimodal self-
regulated learning (SRL) trace data
• Current theoretical, methodological, and analytical advances
• Current and planned projects
• Opportunities for future research using AI to accelerate SRL
• Implications for researchers, learners, educators, and advanced
learning technologies to promote SRL
Overview
Measuring and Fostering Self-Regulated Learning (SRL)
with Advanced Learning Technologies (ALTs)
Theories, models, and
frameworks of Self-
Regulated Learning (SRL)
Context—SRL with
advanced learning
technologies (ALTs)
Measurement of SRL prior to,
during, and following learning,
reasoning, problem solving,
performance, etc.
Analyses of multimodal
multichannel (e.g., eye
tracking, log files) SRL
data
Intelligent and adaptive instructional
interventions to foster self-
regulation and learning, problem
solving, etc. with ALTs
Across Humans, Artificial Agents, Tasks, Domains, and Contexts
Learning, problem solving,
reasoning, understanding, etc.
• Psychological constructs
• What is learning?
• Operational definition(s)
• What are the underlying neural, cognitive, affective,
metacognitive, motivational, social, and contextual
processes?
• When, where, how, and why is learning occurring?
• How do we measure it?
• Research methods
• When, where, how, and why do we measure it?
• How do we analyze it?
• Quantitative, qualitative techniques, mixed methods,
computational modeling
• When, where, how, and why do we analyze learning?
• How do we model it?
• Diagrams, human, artificial human, etc.
• When, where, how, and why do we model learning?
• How do we simulate it?
• Simulation, immersive virtual environments, etc.
• When, where, how, and why do we simulate learning?
Models of Self-Regulated Learning (SRL)
(Azevedo, Bandura, Bjork, D’Mello, Dunlosky, Efklides, Graesser, Greene, Gross,
Hadwin, Järvelä, Kappas, Koriat, Lajoie, Pekrun, Pintrich, Scherer, Schunk, Winne, Zimmerman)
Advanced Learning Technologies
for Self-Regulated Learning (SRL)
MetaTutor (see Azevedo et al., 2022)
Experimental Set-Ups: From Lab to Real, and Virtual World Contexts
Cognitive Processes and Metacognitive Monitoring
(using gaze behavior from an eye tracker; Cloude et al., 2020; Dever et al., in press)
DICE Project (Statistical Reasoning and Misconceptions)
Integration of Multimodal Multichannel Data with MetaTutor
(gaze behavior, cognitive strategies, metacognitive monitoring and judgments, affective responses,
social interactions, context from screen recording)
Detecting, Measuring, and Inferring SRL Processes in Real-Time
(learner AND researcher, teacher, tutor, or trainer)
Learner Researcher (or Teacher, Tutor, or Trainer)
MetaMentor: A System Designed to Enhance Tutors’ and
Teachers’ Understanding of SRL Based on Learners’ Real-
Time Multimodal Data (Azevedo, Lester, et al., 2018)
MetaMentor: A System Designed to Enhance Tutors’ and
Teachers’ Understanding of SRL Based on Learners’ Real-Time
Multimodal Data (Azevedo, Lester, et al., 2018)
Serious Games and Open Learner Models (OLMs)
Inspectable Editable Negotiable
Virtual Learning, Research, Teaching,
Training, and Assessment Platform for SRL
• Explore virtual environments (e.g., Virbela) to detect, track, model,
measure, infer, support, and foster SRL processes of learners
across tasks, domains, and contexts
• Used to teach and train students with embedded intelligent SRL
agents to detect, model, track, support, and foster SRL
• For example, have a Metacognition virtual room with virtual
metacognitive agents capable of:
• Teaching and supporting students’ learning and use of SRL
• Collecting self-report, performance, and trace data on the timing,
frequency of use, efficacy of use, conditions of use, application (e.g.,
success, efficacy), transfer to other tasks, over time, etc.
• Communicating and coordinating with other SRL cognitive,
motivational, and affective agents in their respective virtual rooms to
foster SRL
• Articulating and explaining their own and others’ (i.e., students and
agents) SRL knowledge and skills while living in virtual learning
environments
ZOOMBIES—Simulation of Biological Outbreak
(Dr. Barrie Robinson @ University of Idaho)
Contributions and Limitations of Multimodal SRL Trace Data
Azevedo & Gasevic, 2019; Azevedo & Dever, 2022; Azevedo & Wiedbusch, 2022)
• Most research focuses on log-files as single
channel of SRL process data
• Time-scale of milliseconds to seconds to
sometimes minutes
• Provides mostly static post-hoc analysis but not
the dynamics of SRL processes
• Sequence, frequencies, and durations of
activities, events, interactions, interventions, etc.
• Probability of occurrence for next event
• Mine sequences for dyads, triads, etc. of events
• Generate hypotheses about possible underlying
SRL mechanisms currently not explicit in models
of SRL
• Inferring cognitive and affective processes; but can we
infer metacognitive and motivational processes?
• Assumes equidistance between events (e.g., in log
files), but what about processes with different
durations and are measured at different sampling
rates (e.g., 30Hz vs. 250Hz)?
• Does not capture the parallel nature of SRL process
• Challenging to infer high-level constructs,
assumptions, processes, and mechanisms (e.g.,
adaptivity, dysregulation, self-efficacy, flexibility, etc.)
• Limited use in real-time intelligent interventions (e.g.,
adaptive scaffolding, student modeling) with ALTs
• Not used to measure/detect/infer qualitative and
quantitative changes in SRL over time, tasks, and
contexts
• SRL takes time to develop and needs to be acquired, internalized, practiced over time with the
assistance of human and artificial agents to enhance transfer
• Adaptive (intelligent) scaffolding is key to supporting students’ SRL with learning technologies
• Multimodal multichannel SRL data is key to understanding the dynamics of SRL during learning,
problem solving, reasoning, understanding, etc.
• MetaLearning is key to acquiring, internalizing, using, and transferring SRL knowledge and skills
across tasks, domains, and contexts
• Data visualizations of students’ multimodal SRL processes are key to enhancing their
understanding of SRL and the similar data visualizations are key in designing teacher dashboards
that provide actionable data for effective instructional decision-making
• Cognition, metacognition, and emotions are important for SRL but more attention needs to be paid
to the role of motivation (as states that also fluctuate during task performance)
• Training teachers to learn and use SRL in their classrooms is key in fostering their students’ SRL
• AI-based immersive virtual environments hold great promise to enhance students’ SRL especially
with the use of AI, NLP, computer vision, and machine learning and nanomaterials (e.g., sensors)
Lessons Learned (Azevedo et al., in press)
Current Interdisciplinary Work—UCF SmartLab
• Conceptual and Theoretical Issues
• Define constructs, mechanisms, and CAMM SRL processes
• Integrate current interdisciplinary frameworks, models, and theories of CAMM SRL processes with multimodal multichannel
data (e.g., Azevedo et al., 2019; Azevedo & Dever, 2022; Azevedo & Gasevic, 2019; D’Mello et al., 2018; Efklides, 2018;
Gross, 1015; Järvelä & Bannert, 2021; Lajoie, Pekrun, Azevedo & Leighton, 2020; Panadero, 2017; Pekrun et al., 2011;
Scherer & Moors, 2019; Schunk & Greene, 2018; Winne, 2018; Winne & Azevedo, 2022)
• Methodological and Analytical Issues
• Process-oriented detection, measurement, and analytical methods
• Temporally align and analyze multichannel data but balance theory vs. data-driven approaches
• Temporal dynamics and synchronicity for individual learners and between agents
• Quantitative and qualitative changes in SRL over time
• Continue exploring data mining and machine learning techniques (inferences from high dimensional, and massive and
noisy data sets, chaos theory, etc.)
• Use, design, and test multimodal visualizations for learners, teachers, trainers, and researchers
• Role of Human and Artificial External Regulating Agents
• Role of external regulating agents (e.g., intelligent virtual humans, cyberhumans, nanobots)
• Measure their impact on the acquisition, retention, use, and transfer of learners’ SRL knowledge and skills across topics, tasks, and
contexts
Acknowledgements
• Funding Agencies
• NSF, IES, NIH, DOE, UCF, SSHRC, NSERC, CRC, CFI, CCR, Fulbright, EARLI, and Jacobs Foundation
• Current and former members of the SMART Lab
• Elizabeth Cloude, Megan Wiedbusch, Daryn Dever, Allison Macey, Nikki Ballelos, Dr. Nicholas Mudrick, Megan Price,
Dennis Hernandez, Carina Tudela, Mitchell Moravec, Alex Haikonen, Pooja Ganatra, Sarah Augustine, Daniel Baucom,
Franz Wortha, Kimani Hoffman, Lahari Revuri, Rosy Almanzar, and Jonathan Schertz
• National and international collaborators
• Engin Ader, Anila Asghar, Maria Bannert, Reza Feyzi Behnagh, Gautam Biswas, François Bouchet, Rafael Calvo, Analia
Castigliani, Min Chi, Cristina Conati, Jennifer Cromley, Shane Dawson, Lisa Dieker, Melissa Duffy, Ian Garibay, Dragan
Gašević, Arthur Graesser, Jeffrey A. Greene, Alexander Groeschner, Varadraj Gurupur, Jason Harley, Caridad Hernandez,
Bari Hoffman, Charles Hughes, Eunice Jang, Sanna Järvelä, Joseph Kider, Susanne Lajoie, Joseph LaViola, Ronald
Landis, James Lester, Amanda Major, Rebeca Cerezo Menéndez, Tova Michalsky, Inge Molenaar, Daniel Moos, Krista
Muis, Susanne Narciss, Mark Neider, Soonhye Park, Reinhard Pekrun, Jose Carlos Núñez Pérez, Jonathan Rowe, Michael
Serra, Mindy Shoss, George Siemens, Gale Sinatra, Robert Sottilare, Michelle Taub, Dario Torre, Damla Turgut, Gregory
Trevors, Philip Winne, and Joerg Zumbach
Thank you for your attention
Questions? Collaborations?
roger.azevedo@ucf.edu

More Related Content

Similar to 2022_06_30 «Accelerating Self-Regulated Learning with AI: Opportunities and Challenges»

Learning Analytics
Learning AnalyticsLearning Analytics
Learning AnalyticsViplav Baxi
 
Rae t4 d-knowledge-economy-sa-urs-dec2017
Rae t4 d-knowledge-economy-sa-urs-dec2017Rae t4 d-knowledge-economy-sa-urs-dec2017
Rae t4 d-knowledge-economy-sa-urs-dec2017MYRA School of Business
 
Advances in Learning Analytics and Educational Data Mining
Advances in Learning Analytics and Educational Data Mining Advances in Learning Analytics and Educational Data Mining
Advances in Learning Analytics and Educational Data Mining MehrnooshV
 
Embedding Collective Ownership in a Systems Migration
Embedding Collective Ownership in a Systems MigrationEmbedding Collective Ownership in a Systems Migration
Embedding Collective Ownership in a Systems MigrationNASIG
 
Learning analytics definitions processes potential
Learning analytics definitions processes potentialLearning analytics definitions processes potential
Learning analytics definitions processes potentialFernando Bordignon
 
Learning Analytics
Learning AnalyticsLearning Analytics
Learning AnalyticsJames Little
 
Design a personalized e-learning system based on item response theory and art...
Design a personalized e-learning system based on item response theory and art...Design a personalized e-learning system based on item response theory and art...
Design a personalized e-learning system based on item response theory and art...eraser Juan José Calderón
 
Design a personalized e-learning system based on item response theory and art...
Design a personalized e-learning system based on item response theory and art...Design a personalized e-learning system based on item response theory and art...
Design a personalized e-learning system based on item response theory and art...eraser Juan José Calderón
 
Mehrnoosh vahdat workshop-data sharing 2014
Mehrnoosh vahdat  workshop-data sharing 2014Mehrnoosh vahdat  workshop-data sharing 2014
Mehrnoosh vahdat workshop-data sharing 2014MehrnooshV
 
Embedding Collective Ownership Into a Systems Migration
Embedding Collective Ownership Into a Systems MigrationEmbedding Collective Ownership Into a Systems Migration
Embedding Collective Ownership Into a Systems MigrationTreasa Bane
 
Learning Analytics: Realizing their Promise in the California State University
Learning Analytics:  Realizing their Promise in the California State UniversityLearning Analytics:  Realizing their Promise in the California State University
Learning Analytics: Realizing their Promise in the California State UniversityJohn Whitmer, Ed.D.
 
Snips and snails and puppy dog tails: the need to preserve complexity in math...
Snips and snails and puppy dog tails: the need to preserve complexity in math...Snips and snails and puppy dog tails: the need to preserve complexity in math...
Snips and snails and puppy dog tails: the need to preserve complexity in math...Universidade de Lisboa
 
NCME Big Data in Education
NCME Big Data  in EducationNCME Big Data  in Education
NCME Big Data in EducationPhilip Piety
 
EDUCA Leveraging Analytics FINAL
EDUCA Leveraging Analytics FINALEDUCA Leveraging Analytics FINAL
EDUCA Leveraging Analytics FINALEllen Wagner
 
Student Opportunities- Year 7 to 10 WA Curriculum: Digital Technologies
Student Opportunities- Year 7 to 10 WA Curriculum: Digital TechnologiesStudent Opportunities- Year 7 to 10 WA Curriculum: Digital Technologies
Student Opportunities- Year 7 to 10 WA Curriculum: Digital TechnologiesDr Peter Carey
 

Similar to 2022_06_30 «Accelerating Self-Regulated Learning with AI: Opportunities and Challenges» (20)

Big Data and Student Retention
Big Data and Student RetentionBig Data and Student Retention
Big Data and Student Retention
 
Learning Analytics
Learning AnalyticsLearning Analytics
Learning Analytics
 
Rae t4 d-knowledge-economy-sa-urs-dec2017
Rae t4 d-knowledge-economy-sa-urs-dec2017Rae t4 d-knowledge-economy-sa-urs-dec2017
Rae t4 d-knowledge-economy-sa-urs-dec2017
 
Aiec & csr presentation
Aiec & csr presentationAiec & csr presentation
Aiec & csr presentation
 
Learning Analytics
Learning AnalyticsLearning Analytics
Learning Analytics
 
Advances in Learning Analytics and Educational Data Mining
Advances in Learning Analytics and Educational Data Mining Advances in Learning Analytics and Educational Data Mining
Advances in Learning Analytics and Educational Data Mining
 
Embedding Collective Ownership in a Systems Migration
Embedding Collective Ownership in a Systems MigrationEmbedding Collective Ownership in a Systems Migration
Embedding Collective Ownership in a Systems Migration
 
Learning analytics definitions processes potential
Learning analytics definitions processes potentialLearning analytics definitions processes potential
Learning analytics definitions processes potential
 
Learning Analytics
Learning AnalyticsLearning Analytics
Learning Analytics
 
Design a personalized e-learning system based on item response theory and art...
Design a personalized e-learning system based on item response theory and art...Design a personalized e-learning system based on item response theory and art...
Design a personalized e-learning system based on item response theory and art...
 
Design a personalized e-learning system based on item response theory and art...
Design a personalized e-learning system based on item response theory and art...Design a personalized e-learning system based on item response theory and art...
Design a personalized e-learning system based on item response theory and art...
 
IJET-V2I6P22
IJET-V2I6P22IJET-V2I6P22
IJET-V2I6P22
 
Mehrnoosh vahdat workshop-data sharing 2014
Mehrnoosh vahdat  workshop-data sharing 2014Mehrnoosh vahdat  workshop-data sharing 2014
Mehrnoosh vahdat workshop-data sharing 2014
 
Embedding Collective Ownership Into a Systems Migration
Embedding Collective Ownership Into a Systems MigrationEmbedding Collective Ownership Into a Systems Migration
Embedding Collective Ownership Into a Systems Migration
 
Ba education
Ba educationBa education
Ba education
 
Learning Analytics: Realizing their Promise in the California State University
Learning Analytics:  Realizing their Promise in the California State UniversityLearning Analytics:  Realizing their Promise in the California State University
Learning Analytics: Realizing their Promise in the California State University
 
Snips and snails and puppy dog tails: the need to preserve complexity in math...
Snips and snails and puppy dog tails: the need to preserve complexity in math...Snips and snails and puppy dog tails: the need to preserve complexity in math...
Snips and snails and puppy dog tails: the need to preserve complexity in math...
 
NCME Big Data in Education
NCME Big Data  in EducationNCME Big Data  in Education
NCME Big Data in Education
 
EDUCA Leveraging Analytics FINAL
EDUCA Leveraging Analytics FINALEDUCA Leveraging Analytics FINAL
EDUCA Leveraging Analytics FINAL
 
Student Opportunities- Year 7 to 10 WA Curriculum: Digital Technologies
Student Opportunities- Year 7 to 10 WA Curriculum: Digital TechnologiesStudent Opportunities- Year 7 to 10 WA Curriculum: Digital Technologies
Student Opportunities- Year 7 to 10 WA Curriculum: Digital Technologies
 

More from eMadrid network

Recognizing Lifelong Learning Competences: A Report of Two Cases - Edmundo Tovar
Recognizing Lifelong Learning Competences: A Report of Two Cases - Edmundo TovarRecognizing Lifelong Learning Competences: A Report of Two Cases - Edmundo Tovar
Recognizing Lifelong Learning Competences: A Report of Two Cases - Edmundo TovareMadrid network
 
A study about the impact of rewards on student's engagement with the flipped ...
A study about the impact of rewards on student's engagement with the flipped ...A study about the impact of rewards on student's engagement with the flipped ...
A study about the impact of rewards on student's engagement with the flipped ...eMadrid network
 
Assessment and recognition in technical massive open on-line courses with and...
Assessment and recognition in technical massive open on-line courses with and...Assessment and recognition in technical massive open on-line courses with and...
Assessment and recognition in technical massive open on-line courses with and...eMadrid network
 
Recognition of learning: Status, experiences and challenges - Carlos Delgado ...
Recognition of learning: Status, experiences and challenges - Carlos Delgado ...Recognition of learning: Status, experiences and challenges - Carlos Delgado ...
Recognition of learning: Status, experiences and challenges - Carlos Delgado ...eMadrid network
 
Bootstrapping serious games to assess learning through analytics - Baltasar F...
Bootstrapping serious games to assess learning through analytics - Baltasar F...Bootstrapping serious games to assess learning through analytics - Baltasar F...
Bootstrapping serious games to assess learning through analytics - Baltasar F...eMadrid network
 
Meta-review of recognition of learning in LMS and MOOCs - Ruth Cobos
Meta-review of recognition of learning in LMS and MOOCs - Ruth CobosMeta-review of recognition of learning in LMS and MOOCs - Ruth Cobos
Meta-review of recognition of learning in LMS and MOOCs - Ruth CoboseMadrid network
 
Best paper Award - Miguel Castro
Best paper Award - Miguel CastroBest paper Award - Miguel Castro
Best paper Award - Miguel CastroeMadrid network
 
eMadrid Gaming4Coding - Possibilities of game learning analytics for coding l...
eMadrid Gaming4Coding - Possibilities of game learning analytics for coding l...eMadrid Gaming4Coding - Possibilities of game learning analytics for coding l...
eMadrid Gaming4Coding - Possibilities of game learning analytics for coding l...eMadrid network
 
Seminario eMadrid_Curso MOOC_Antonio de Nebrija_Apología del saber.pptx.pdf
Seminario eMadrid_Curso MOOC_Antonio de Nebrija_Apología del saber.pptx.pdfSeminario eMadrid_Curso MOOC_Antonio de Nebrija_Apología del saber.pptx.pdf
Seminario eMadrid_Curso MOOC_Antonio de Nebrija_Apología del saber.pptx.pdfeMadrid network
 
eMadrid-Opportunities and Design Challenges in the Gaming4Coding Project_Pete...
eMadrid-Opportunities and Design Challenges in the Gaming4Coding Project_Pete...eMadrid-Opportunities and Design Challenges in the Gaming4Coding Project_Pete...
eMadrid-Opportunities and Design Challenges in the Gaming4Coding Project_Pete...eMadrid network
 
Open_principles_and_co-creation_for_digital_competences_for_students.pdf
Open_principles_and_co-creation_for_digital_competences_for_students.pdfOpen_principles_and_co-creation_for_digital_competences_for_students.pdf
Open_principles_and_co-creation_for_digital_competences_for_students.pdfeMadrid network
 
Competencias_digitales_del_profesorado_universitario_para_la_educación_abiert...
Competencias_digitales_del_profesorado_universitario_para_la_educación_abiert...Competencias_digitales_del_profesorado_universitario_para_la_educación_abiert...
Competencias_digitales_del_profesorado_universitario_para_la_educación_abiert...eMadrid network
 
eMadrid_KatjaAssaf_DigiCred.pdf
eMadrid_KatjaAssaf_DigiCred.pdfeMadrid_KatjaAssaf_DigiCred.pdf
eMadrid_KatjaAssaf_DigiCred.pdfeMadrid network
 
Presentazione E-Madrid - 12-01-2023 Ruth Kerr.pdf
Presentazione E-Madrid - 12-01-2023 Ruth Kerr.pdfPresentazione E-Madrid - 12-01-2023 Ruth Kerr.pdf
Presentazione E-Madrid - 12-01-2023 Ruth Kerr.pdfeMadrid network
 
EDC-eMadrid_20230113 Ildikó Mázár.pdf
EDC-eMadrid_20230113 Ildikó Mázár.pdfEDC-eMadrid_20230113 Ildikó Mázár.pdf
EDC-eMadrid_20230113 Ildikó Mázár.pdfeMadrid network
 
2022_12_16 «“La informática en la educación escolar en Europa”, informe Euryd...
2022_12_16 «“La informática en la educación escolar en Europa”, informe Euryd...2022_12_16 «“La informática en la educación escolar en Europa”, informe Euryd...
2022_12_16 «“La informática en la educación escolar en Europa”, informe Euryd...eMadrid network
 
2022_12_16 «Informatics – A Fundamental Discipline for the 21st Century»
2022_12_16 «Informatics – A Fundamental Discipline for the 21st Century»2022_12_16 «Informatics – A Fundamental Discipline for the 21st Century»
2022_12_16 «Informatics – A Fundamental Discipline for the 21st Century»eMadrid network
 
2022_12_16 «Efecto del uso de lenguajes basados en bloques en el aprendizaje ...
2022_12_16 «Efecto del uso de lenguajes basados en bloques en el aprendizaje ...2022_12_16 «Efecto del uso de lenguajes basados en bloques en el aprendizaje ...
2022_12_16 «Efecto del uso de lenguajes basados en bloques en el aprendizaje ...eMadrid network
 
2022_11_11 «AI and ML methods for Multimodal Learning Analytics»
2022_11_11 «AI and ML methods for Multimodal Learning Analytics»2022_11_11 «AI and ML methods for Multimodal Learning Analytics»
2022_11_11 «AI and ML methods for Multimodal Learning Analytics»eMadrid network
 
2022_11_11 «The promise and challenges of Multimodal Learning Analytics»
2022_11_11 «The promise and challenges of Multimodal Learning Analytics»2022_11_11 «The promise and challenges of Multimodal Learning Analytics»
2022_11_11 «The promise and challenges of Multimodal Learning Analytics»eMadrid network
 

More from eMadrid network (20)

Recognizing Lifelong Learning Competences: A Report of Two Cases - Edmundo Tovar
Recognizing Lifelong Learning Competences: A Report of Two Cases - Edmundo TovarRecognizing Lifelong Learning Competences: A Report of Two Cases - Edmundo Tovar
Recognizing Lifelong Learning Competences: A Report of Two Cases - Edmundo Tovar
 
A study about the impact of rewards on student's engagement with the flipped ...
A study about the impact of rewards on student's engagement with the flipped ...A study about the impact of rewards on student's engagement with the flipped ...
A study about the impact of rewards on student's engagement with the flipped ...
 
Assessment and recognition in technical massive open on-line courses with and...
Assessment and recognition in technical massive open on-line courses with and...Assessment and recognition in technical massive open on-line courses with and...
Assessment and recognition in technical massive open on-line courses with and...
 
Recognition of learning: Status, experiences and challenges - Carlos Delgado ...
Recognition of learning: Status, experiences and challenges - Carlos Delgado ...Recognition of learning: Status, experiences and challenges - Carlos Delgado ...
Recognition of learning: Status, experiences and challenges - Carlos Delgado ...
 
Bootstrapping serious games to assess learning through analytics - Baltasar F...
Bootstrapping serious games to assess learning through analytics - Baltasar F...Bootstrapping serious games to assess learning through analytics - Baltasar F...
Bootstrapping serious games to assess learning through analytics - Baltasar F...
 
Meta-review of recognition of learning in LMS and MOOCs - Ruth Cobos
Meta-review of recognition of learning in LMS and MOOCs - Ruth CobosMeta-review of recognition of learning in LMS and MOOCs - Ruth Cobos
Meta-review of recognition of learning in LMS and MOOCs - Ruth Cobos
 
Best paper Award - Miguel Castro
Best paper Award - Miguel CastroBest paper Award - Miguel Castro
Best paper Award - Miguel Castro
 
eMadrid Gaming4Coding - Possibilities of game learning analytics for coding l...
eMadrid Gaming4Coding - Possibilities of game learning analytics for coding l...eMadrid Gaming4Coding - Possibilities of game learning analytics for coding l...
eMadrid Gaming4Coding - Possibilities of game learning analytics for coding l...
 
Seminario eMadrid_Curso MOOC_Antonio de Nebrija_Apología del saber.pptx.pdf
Seminario eMadrid_Curso MOOC_Antonio de Nebrija_Apología del saber.pptx.pdfSeminario eMadrid_Curso MOOC_Antonio de Nebrija_Apología del saber.pptx.pdf
Seminario eMadrid_Curso MOOC_Antonio de Nebrija_Apología del saber.pptx.pdf
 
eMadrid-Opportunities and Design Challenges in the Gaming4Coding Project_Pete...
eMadrid-Opportunities and Design Challenges in the Gaming4Coding Project_Pete...eMadrid-Opportunities and Design Challenges in the Gaming4Coding Project_Pete...
eMadrid-Opportunities and Design Challenges in the Gaming4Coding Project_Pete...
 
Open_principles_and_co-creation_for_digital_competences_for_students.pdf
Open_principles_and_co-creation_for_digital_competences_for_students.pdfOpen_principles_and_co-creation_for_digital_competences_for_students.pdf
Open_principles_and_co-creation_for_digital_competences_for_students.pdf
 
Competencias_digitales_del_profesorado_universitario_para_la_educación_abiert...
Competencias_digitales_del_profesorado_universitario_para_la_educación_abiert...Competencias_digitales_del_profesorado_universitario_para_la_educación_abiert...
Competencias_digitales_del_profesorado_universitario_para_la_educación_abiert...
 
eMadrid_KatjaAssaf_DigiCred.pdf
eMadrid_KatjaAssaf_DigiCred.pdfeMadrid_KatjaAssaf_DigiCred.pdf
eMadrid_KatjaAssaf_DigiCred.pdf
 
Presentazione E-Madrid - 12-01-2023 Ruth Kerr.pdf
Presentazione E-Madrid - 12-01-2023 Ruth Kerr.pdfPresentazione E-Madrid - 12-01-2023 Ruth Kerr.pdf
Presentazione E-Madrid - 12-01-2023 Ruth Kerr.pdf
 
EDC-eMadrid_20230113 Ildikó Mázár.pdf
EDC-eMadrid_20230113 Ildikó Mázár.pdfEDC-eMadrid_20230113 Ildikó Mázár.pdf
EDC-eMadrid_20230113 Ildikó Mázár.pdf
 
2022_12_16 «“La informática en la educación escolar en Europa”, informe Euryd...
2022_12_16 «“La informática en la educación escolar en Europa”, informe Euryd...2022_12_16 «“La informática en la educación escolar en Europa”, informe Euryd...
2022_12_16 «“La informática en la educación escolar en Europa”, informe Euryd...
 
2022_12_16 «Informatics – A Fundamental Discipline for the 21st Century»
2022_12_16 «Informatics – A Fundamental Discipline for the 21st Century»2022_12_16 «Informatics – A Fundamental Discipline for the 21st Century»
2022_12_16 «Informatics – A Fundamental Discipline for the 21st Century»
 
2022_12_16 «Efecto del uso de lenguajes basados en bloques en el aprendizaje ...
2022_12_16 «Efecto del uso de lenguajes basados en bloques en el aprendizaje ...2022_12_16 «Efecto del uso de lenguajes basados en bloques en el aprendizaje ...
2022_12_16 «Efecto del uso de lenguajes basados en bloques en el aprendizaje ...
 
2022_11_11 «AI and ML methods for Multimodal Learning Analytics»
2022_11_11 «AI and ML methods for Multimodal Learning Analytics»2022_11_11 «AI and ML methods for Multimodal Learning Analytics»
2022_11_11 «AI and ML methods for Multimodal Learning Analytics»
 
2022_11_11 «The promise and challenges of Multimodal Learning Analytics»
2022_11_11 «The promise and challenges of Multimodal Learning Analytics»2022_11_11 «The promise and challenges of Multimodal Learning Analytics»
2022_11_11 «The promise and challenges of Multimodal Learning Analytics»
 

Recently uploaded

A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersNicole Novielli
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Exploring ChatGPT Prompt Hacks To Maximally Optimise Your Queries
Exploring ChatGPT Prompt Hacks To Maximally Optimise Your QueriesExploring ChatGPT Prompt Hacks To Maximally Optimise Your Queries
Exploring ChatGPT Prompt Hacks To Maximally Optimise Your QueriesSanjay Willie
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Hiroshi SHIBATA
 
Scale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterScale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterMydbops
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfLoriGlavin3
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch TuesdayIvanti
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI AgeCprime
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...Scott Andery
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...AliaaTarek5
 

Recently uploaded (20)

A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software Developers
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
Exploring ChatGPT Prompt Hacks To Maximally Optimise Your Queries
Exploring ChatGPT Prompt Hacks To Maximally Optimise Your QueriesExploring ChatGPT Prompt Hacks To Maximally Optimise Your Queries
Exploring ChatGPT Prompt Hacks To Maximally Optimise Your Queries
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024
 
Scale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterScale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL Router
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdf
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch Tuesday
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI Age
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
 

2022_06_30 «Accelerating Self-Regulated Learning with AI: Opportunities and Challenges»

  • 1. Roger Azevedo, Ph.D. University of Central Florida School of Modeling, Simulation, and Training Learning Sciences Faculty Cluster Initiative Departments of Computer Science and Internal Medicine Laboratory for the Study of Metacognition and Advanced Learning Technologies Accelerating Self-Regulated Learning with AI: Opportunities and Challenges
  • 2. • Science of learning with technology and multimodal self- regulated learning (SRL) trace data • Current theoretical, methodological, and analytical advances • Current and planned projects • Opportunities for future research using AI to accelerate SRL • Implications for researchers, learners, educators, and advanced learning technologies to promote SRL Overview
  • 3. Measuring and Fostering Self-Regulated Learning (SRL) with Advanced Learning Technologies (ALTs) Theories, models, and frameworks of Self- Regulated Learning (SRL) Context—SRL with advanced learning technologies (ALTs) Measurement of SRL prior to, during, and following learning, reasoning, problem solving, performance, etc. Analyses of multimodal multichannel (e.g., eye tracking, log files) SRL data Intelligent and adaptive instructional interventions to foster self- regulation and learning, problem solving, etc. with ALTs Across Humans, Artificial Agents, Tasks, Domains, and Contexts
  • 4. Learning, problem solving, reasoning, understanding, etc. • Psychological constructs • What is learning? • Operational definition(s) • What are the underlying neural, cognitive, affective, metacognitive, motivational, social, and contextual processes? • When, where, how, and why is learning occurring? • How do we measure it? • Research methods • When, where, how, and why do we measure it? • How do we analyze it? • Quantitative, qualitative techniques, mixed methods, computational modeling • When, where, how, and why do we analyze learning? • How do we model it? • Diagrams, human, artificial human, etc. • When, where, how, and why do we model learning? • How do we simulate it? • Simulation, immersive virtual environments, etc. • When, where, how, and why do we simulate learning?
  • 5. Models of Self-Regulated Learning (SRL) (Azevedo, Bandura, Bjork, D’Mello, Dunlosky, Efklides, Graesser, Greene, Gross, Hadwin, Järvelä, Kappas, Koriat, Lajoie, Pekrun, Pintrich, Scherer, Schunk, Winne, Zimmerman)
  • 6. Advanced Learning Technologies for Self-Regulated Learning (SRL)
  • 7. MetaTutor (see Azevedo et al., 2022)
  • 8. Experimental Set-Ups: From Lab to Real, and Virtual World Contexts
  • 9. Cognitive Processes and Metacognitive Monitoring (using gaze behavior from an eye tracker; Cloude et al., 2020; Dever et al., in press)
  • 10. DICE Project (Statistical Reasoning and Misconceptions)
  • 11. Integration of Multimodal Multichannel Data with MetaTutor (gaze behavior, cognitive strategies, metacognitive monitoring and judgments, affective responses, social interactions, context from screen recording)
  • 12. Detecting, Measuring, and Inferring SRL Processes in Real-Time (learner AND researcher, teacher, tutor, or trainer) Learner Researcher (or Teacher, Tutor, or Trainer)
  • 13. MetaMentor: A System Designed to Enhance Tutors’ and Teachers’ Understanding of SRL Based on Learners’ Real- Time Multimodal Data (Azevedo, Lester, et al., 2018)
  • 14. MetaMentor: A System Designed to Enhance Tutors’ and Teachers’ Understanding of SRL Based on Learners’ Real-Time Multimodal Data (Azevedo, Lester, et al., 2018)
  • 15. Serious Games and Open Learner Models (OLMs) Inspectable Editable Negotiable
  • 16. Virtual Learning, Research, Teaching, Training, and Assessment Platform for SRL • Explore virtual environments (e.g., Virbela) to detect, track, model, measure, infer, support, and foster SRL processes of learners across tasks, domains, and contexts • Used to teach and train students with embedded intelligent SRL agents to detect, model, track, support, and foster SRL • For example, have a Metacognition virtual room with virtual metacognitive agents capable of: • Teaching and supporting students’ learning and use of SRL • Collecting self-report, performance, and trace data on the timing, frequency of use, efficacy of use, conditions of use, application (e.g., success, efficacy), transfer to other tasks, over time, etc. • Communicating and coordinating with other SRL cognitive, motivational, and affective agents in their respective virtual rooms to foster SRL • Articulating and explaining their own and others’ (i.e., students and agents) SRL knowledge and skills while living in virtual learning environments
  • 17. ZOOMBIES—Simulation of Biological Outbreak (Dr. Barrie Robinson @ University of Idaho)
  • 18. Contributions and Limitations of Multimodal SRL Trace Data Azevedo & Gasevic, 2019; Azevedo & Dever, 2022; Azevedo & Wiedbusch, 2022) • Most research focuses on log-files as single channel of SRL process data • Time-scale of milliseconds to seconds to sometimes minutes • Provides mostly static post-hoc analysis but not the dynamics of SRL processes • Sequence, frequencies, and durations of activities, events, interactions, interventions, etc. • Probability of occurrence for next event • Mine sequences for dyads, triads, etc. of events • Generate hypotheses about possible underlying SRL mechanisms currently not explicit in models of SRL • Inferring cognitive and affective processes; but can we infer metacognitive and motivational processes? • Assumes equidistance between events (e.g., in log files), but what about processes with different durations and are measured at different sampling rates (e.g., 30Hz vs. 250Hz)? • Does not capture the parallel nature of SRL process • Challenging to infer high-level constructs, assumptions, processes, and mechanisms (e.g., adaptivity, dysregulation, self-efficacy, flexibility, etc.) • Limited use in real-time intelligent interventions (e.g., adaptive scaffolding, student modeling) with ALTs • Not used to measure/detect/infer qualitative and quantitative changes in SRL over time, tasks, and contexts
  • 19. • SRL takes time to develop and needs to be acquired, internalized, practiced over time with the assistance of human and artificial agents to enhance transfer • Adaptive (intelligent) scaffolding is key to supporting students’ SRL with learning technologies • Multimodal multichannel SRL data is key to understanding the dynamics of SRL during learning, problem solving, reasoning, understanding, etc. • MetaLearning is key to acquiring, internalizing, using, and transferring SRL knowledge and skills across tasks, domains, and contexts • Data visualizations of students’ multimodal SRL processes are key to enhancing their understanding of SRL and the similar data visualizations are key in designing teacher dashboards that provide actionable data for effective instructional decision-making • Cognition, metacognition, and emotions are important for SRL but more attention needs to be paid to the role of motivation (as states that also fluctuate during task performance) • Training teachers to learn and use SRL in their classrooms is key in fostering their students’ SRL • AI-based immersive virtual environments hold great promise to enhance students’ SRL especially with the use of AI, NLP, computer vision, and machine learning and nanomaterials (e.g., sensors) Lessons Learned (Azevedo et al., in press)
  • 20. Current Interdisciplinary Work—UCF SmartLab • Conceptual and Theoretical Issues • Define constructs, mechanisms, and CAMM SRL processes • Integrate current interdisciplinary frameworks, models, and theories of CAMM SRL processes with multimodal multichannel data (e.g., Azevedo et al., 2019; Azevedo & Dever, 2022; Azevedo & Gasevic, 2019; D’Mello et al., 2018; Efklides, 2018; Gross, 1015; Järvelä & Bannert, 2021; Lajoie, Pekrun, Azevedo & Leighton, 2020; Panadero, 2017; Pekrun et al., 2011; Scherer & Moors, 2019; Schunk & Greene, 2018; Winne, 2018; Winne & Azevedo, 2022) • Methodological and Analytical Issues • Process-oriented detection, measurement, and analytical methods • Temporally align and analyze multichannel data but balance theory vs. data-driven approaches • Temporal dynamics and synchronicity for individual learners and between agents • Quantitative and qualitative changes in SRL over time • Continue exploring data mining and machine learning techniques (inferences from high dimensional, and massive and noisy data sets, chaos theory, etc.) • Use, design, and test multimodal visualizations for learners, teachers, trainers, and researchers • Role of Human and Artificial External Regulating Agents • Role of external regulating agents (e.g., intelligent virtual humans, cyberhumans, nanobots) • Measure their impact on the acquisition, retention, use, and transfer of learners’ SRL knowledge and skills across topics, tasks, and contexts
  • 21. Acknowledgements • Funding Agencies • NSF, IES, NIH, DOE, UCF, SSHRC, NSERC, CRC, CFI, CCR, Fulbright, EARLI, and Jacobs Foundation • Current and former members of the SMART Lab • Elizabeth Cloude, Megan Wiedbusch, Daryn Dever, Allison Macey, Nikki Ballelos, Dr. Nicholas Mudrick, Megan Price, Dennis Hernandez, Carina Tudela, Mitchell Moravec, Alex Haikonen, Pooja Ganatra, Sarah Augustine, Daniel Baucom, Franz Wortha, Kimani Hoffman, Lahari Revuri, Rosy Almanzar, and Jonathan Schertz • National and international collaborators • Engin Ader, Anila Asghar, Maria Bannert, Reza Feyzi Behnagh, Gautam Biswas, François Bouchet, Rafael Calvo, Analia Castigliani, Min Chi, Cristina Conati, Jennifer Cromley, Shane Dawson, Lisa Dieker, Melissa Duffy, Ian Garibay, Dragan Gašević, Arthur Graesser, Jeffrey A. Greene, Alexander Groeschner, Varadraj Gurupur, Jason Harley, Caridad Hernandez, Bari Hoffman, Charles Hughes, Eunice Jang, Sanna Järvelä, Joseph Kider, Susanne Lajoie, Joseph LaViola, Ronald Landis, James Lester, Amanda Major, Rebeca Cerezo Menéndez, Tova Michalsky, Inge Molenaar, Daniel Moos, Krista Muis, Susanne Narciss, Mark Neider, Soonhye Park, Reinhard Pekrun, Jose Carlos Núñez Pérez, Jonathan Rowe, Michael Serra, Mindy Shoss, George Siemens, Gale Sinatra, Robert Sottilare, Michelle Taub, Dario Torre, Damla Turgut, Gregory Trevors, Philip Winne, and Joerg Zumbach Thank you for your attention Questions? Collaborations? roger.azevedo@ucf.edu