SlideShare a Scribd company logo
Dr. Mutlu Cukurova
University College London
m.cukurova@ucl.ac.uk
@mutlucukurova
The Promise and Challenges
of Multimodal Learning Analytics
Three conceptualisations of MMLA
1. MMLA to
Automate Human
Tasks
2. MMLA to
Augment Teaching
and Learning
Practices
3. MMLA as a
Research
Methodology
“tools that have been designed and developed
to replace human tasks through analysis of
multiple modalities of data, and prediction of
the best value for a designated outcome
variable, which is conveyed through a user
interface.”
Multimodal Learning Analytics as:
What Modalities of Data We Use?
0
5
10
15
20
25
30
35
40
Presenter’s pose,
gaze direction, visual
attention
Audio, dialogue’s
characteristics
Facialdata and
emotions
Posture and gesture,
body movement,
distance, motion,
Position
Learners’
concentration level,
visual attention and
habits
Learners’ gaze in off-
screen activity
Student’s
physiological data,
arousal and EDA
Students’ cognitive
load
Posture and gesture Location, position
and duration,
movement
Sitting position EEG data, brain
activity
Video and audio Eye-tracking Skin sensing In depth camera Location sensing Pressure sensing EEG sensor
Number of published articles 2010 - 2020
Alwahaby, H., Cukurova, M., Papamitsiou, Z., & Giannakos, M. (2021). The evidence of impact and ethical considerations of Multimodal Learning Analytics:
A Systematic Literature Review. https://doi.org/10.35542/osf.io/sd23y
MMLA as Machine Learning Tools
Human
Control
Automation through AI
Most traditional
Educational Technology
AH = Human tasks are replaced
by MMLA H ß A
Initial vision of AIED: Early
Promises of Intelligent Tutoring
Systems
High
Low High
Low
Machine Learning Classification of CPS Competence
Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018). Supervised machine learning in multimodal learning analytics for estimating success in project-based learning.
Journal of Computer Assisted Learning, 34(4), 366-377.
Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018). Supervised
machine learning in multimodal learning analytics for estimating success in
project-based learning. Journal of Computer Assisted Learning, 34(4), 366-377.
The promise of MMLA
R. Kawamura et al., Detecting Drowsy Learners at the Wheel of e-Learning Platforms With Multimodal Learning Analytics, IEEE Access, vol. 9, pp. 115165-115174, 2021, doi:
10.1109/ACCESS.2021.3104805
“to support learning experiences
that may be collaborative,
hands-on, and face-to-face,
de-emphasizing the computer
screen as the primary form or
object of interaction.”
Multimodal Learning Analytics:
Worsley, M., Martinez-Maldonado, R., & D'Angelo, C. (2021). "A New Era in Multimodal Learning Analytics: Twelve Core Commitments to Ground and Grow
MMLA." Journal of Learning Analytics 8.3, 10-27.
2. MMLA to
Augment Teaching
and Learning
Practices
MMLA to Augment Teaching and Learning
Practices
Human
Control
Automation through AI
Most traditional
Educational Technology
HA = Humans internalise MMLA
models H à A
Analytics and GOFAI: Changing
the operations and
representations of thought
AH = Human tasks are replaced
by AI H ß A
Initial vision of AIED: Early
Promises of Intelligent Tutoring
Systems
High
Low
High
Low
17
Cukurova, M., Kent, C., & Luckin, R. (2019). Artificial Intelligence and Multimodal Data in the Service of Human Decision-making: A case Study in Debate
Tutoring, British Journal of Educational Technology, 50 (6), 3032-3046.
Models as learning affordances for humans
Kent, C., Chaudhry, M., Cukurova, M. Bashir, I., Pickard, H., Jenkins, C., du Boulay, B., Luckin, R., (2021). Machine learning models and their development process as learning
affordances for humans. International Conference of Artificial Intelligence in Education, Springer, Cham.
What do successful students look like?
What are their behaviour and personality traits?
Mental model : diagram of functionality
Initial student profiles
Active
Active
Passive
Kasparova, A., Celiktutan, O., & Cukurova, M. (2020). Inferring Student Engagement in Collaborative Problem Solving from Visual Cues. Companion Publication of the 2020
International Conference on Multimodal Interaction. https://doi.org/10.1145/3395035.3425961
From machine observations to learning
sciences constructs of collaboration
Interpretationsof Collaboration Competence from Ma
Observables
Cukurova, M., Luckin, R., Millán, E., & Mavrikis, M. (2018). The NISPI framework: Analysing collaborative problem-solvin
students' physical interactions. Computers & Education, 116, 93-109.
Some important components of collaboration may be interpreted through
automated machine observable data:
• synchrony,
• individual accountability,
• equality,
• mutuality.
Cukurova, M., Luckin, R., Millán, E., & Mavrikis, M. (2018). The NISPI framework: Analysing collaborative problem-solving from students' physical
interactions. Computers & Education, 116, 93-109.
Cukurova, M. (2018). A syllogism for designing collaborative learning technologies in the age of AI and multimodal data. In European Conference on Technology
Enhanced Learning (pp. 291-296). Springer, Cham.
Cukurova, M., Luckin, R., Millán, E., & Mavrikis, M. (2018). The NISPI framework: Analysing collaborative problem-solving from students' physical
interactions. Computers & Education, 116, 93-109.
14
Cukurova, M., Mavrikis, M., & Luckin, R. (2017). Evidence-Centered Design and Its Application to Collaborative Problem Solving in Practice-based
Learning Environments. Analytics4Learning. Stanford Research Institute, Menlo Park, USA.
Learning sciences should drive the features.
Learner/Teachers’ needs and expectations should be met.
Kent, C., & Cukurova, M. (2020). Investigating Collaboration as a Process with Theory-driven Learning Analytics. Journal of Learning Analytics, 7(1), 59-71.
Cukurova, M., Kent, C., & Akanji, A. (2022). Identifying tertiary level educators' needs and understanding of the collaboration process analytics. https://doi.org/10.35542/osf.io/2d3ty
MMLA as a Research Methodology
3.
MMLA as a Research
Methodology
Ouyang, F., Xu, W., Cukurova, M. (2022). An Artificial Intelligence-driven Learning Analytics Method to Examine the Collaborative Problem-solving Process from the Complex Adaptive Systems
Perspective, International Journal of Computer Supported Collaborative Learning. http://arxiv.org/abs/2210.16059v1
Interactive Peer interaction through
communications (Int-C)
Peer interaction through behaviours
(Int-B)
Cognitive Superficial-level knowledge (KS)
Medium-level knowledge (KM)
Deep-level knowledge (KD)
Behavioural Resource management (RM)
Concept mapping (CM)
Observation (OB)
Regulative Task understanding (TU)
Goal setting and planning (GSP)
Monitoring and reflection (MR)
Socio-emotional Active listening and respect (ALR)
Encouraging participation and
inclusion (EPI)
Fostering cohesion (FC)
• Behaviour-oriented collaborative pattern (Type 1) - Medium-level performance
• Communication-behaviour-synergistic collaborative pattern (Type 2) – High-level performance
• Communication-oriented collaborative pattern (Type 3) – Low-level performance
Ouyang, F., Xu, W., Cukurova, M. (2022). An Artificial Intelligence-driven Learning Analytics Method to Examine the Collaborative Problem-solving Process from the Complex
Adaptive Systems Perspective, International Journal of Computer Supported Collaborative Learning. http://arxiv.org/abs/2210.16059v1
• Peer-interaction focussed
collaborative pattern (PIF Type)
• Resources-interaction focusses
collaborative pattern (RIF Type)
Zhou, Q., Suraworachet, W., Celiktutan, O., & Cukurova, M. (2022). What Does Shared Understanding in Students’ Face-to-Face Collaborative Learning Gaze Behaviours “Look
Like”?. In International Conference on Artificial Intelligence in Education (pp. 588-593). Springer, Cham.
1. Methodological - ML AI is not that
intelligent - AIP
2. Logistical/practical/financial
3. Connection to learning theory and
learning design
Short-term Challenges of MMLA
Cukurova, M., Giannakos, M., & Martinez-Maldonado, R. (2020). The Promise and Challenges of Multimodal Learning Analytics. British Journal of Educational
Technology, 51(5), pp. 1441-1449, https://doi.org/10.1111/bjet.13015.
Zhou, Q., Suraworachet, W., Cukurova, M. (2021). Different modality, different design, different results: exploring self-regulated learner clusters’ engagement behaviours at
individual, group and cohort activities. https://doi.org/10.35542/osf.io/u3g4n
“More Significant” Challenges of MMLA –
Particularly for the first conceptualisation
Normativity
1. What behaviours are
good/bad in education?
2. Inference based on history
3. Flourishing Diversity/ in
uncertainty
Prediction
1. What is it good
for/intervention?
2. Fairness
3. Transparency
4. Accountability
Human Agency
1. Balancing
exploration and
exploitation
2. The reward
function
3. Privacy/
surveillance
Ethics-
Human
Values
Alwahaby, H., Cukurova, M., Papamitsiou, Z., & Giannakos, M. (2022). The Evidence of Impact and Ethical Considerations of Multimodal Learning Analytics: A Systematic
Literature Review. In The Multimodal Learning Analytics Handbook (pp. 289-325). Springer International Publishing. doi:10.1007/978-3-031-08076-0_12
Students have mixed reactions to MMLA
Zhou,Q., Suraworachet, W., Pozdniakov, S., Martinez-Maldonado, R., Bartindale, T., Chen, P. Richardson, D., & Cukurova M. (2021). Investigating Students’ Experiences with Collaboration
Analytics for Remote Group Meetings. International Conference of Artificial Intelligence in Education, Springer, Cham.
Zhou, Q., Suraworachet, W., Cukurova, M. (2021). Different modality, different design, different results: exploring self-regulated learner clusters’ engagement behaviours at individual, group
and cohort activities. https://doi.org/10.35542/osf.io/u3g4n
- Were not concerned (or
faded) due to: the module
domain, invisibility, not
parts of sum assessment
- More concerning for low
contributors
- Motivational for increased
accountability for high
contributors
Nazaretsky, T., Cukurova, M., Ariely, M., & Alexandron, G. (2021). Confirmation bias and trust: Human factors that influence teachers' attitudes towards AI-based educational
technology, CEUR Workshop Proceedings (Vol. 3042).
Cukurova, M., Luckin, R., & Kent, C. (2019). Impact of an Artificial Intelligence Research Frame on the Perceived Credibility of Educational Research Evidence. International Journal
of Artificial Intelligence in Education, 1-31.
Teachers have confirmation bias and unrealistic
expectations from AI-based EdTech.
"AI framing effect": when people (including
teachers) are presented with research evidence
framed as coming from AIED, they tend to judge it
as less credible compared to educational
psychology and neuroscience.
Teachers appear to be more critical
How do we gain teachers’ trust in MMLA?
• Developed a survey instrument that revealed eight dimensions.
Alwahaby, H., Cukurova, M., Papamitsiou, Z., & Giannakos, M. (2021). The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review. https://doi.org/10.35542/osf.io/sd23y
Nazaretsky, T., Cukurova, M., Alexandron, G. (2022). An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology. Learning Analytics and Knowledge Conference. ACM: New York.
1. Perceived Benefits
2. Lack of Human Characteristics
3. Lack of Transparency
4. Anxieties
5. Self-efficacy
6. Required Shift in Pedagogy
7. Means to Increase Trust
8. AI-EdTech vs Human Advice
Strongly
not agree
Not agree Strongly
agree
Agree
Neutral
How would a CPD to increase teachers’ trust in
intelligent technologies look like?
Nazaretsky, T., Ariely, M., Cukurova, M., Alexandron, G. (2022). Teachers’ Trust in AI-powered Educational Technology and a Professional Development Program to Improve It, British Journal of Educational
Technology, DOI: 10.1111/bjet.13232
What do we mean by transparency?
Transparency for whom?
Chaudhry, M. A., Cukurova, M., & Luckin, R. (2022). A Transparency Index Framework for AI in Education. Artificial Intelligence in Education. https://arxiv.org/abs/2206.03220 .
Cukurova, M., Zhou, Q., Spikol, D., & Landolfi, L. (2020). Modelling collaborative problem-solving competence with transparent learning analytics: is video data enough?. In Proceedings of the
Tenth International Conference on Learning Analytics & Knowledge (pp. 270-275).
Listening Speaking
Transparent decision trees to model
Collaborative problem solving competence
Watching Making Collaborative problem-solving competence
The Golden Triangle
for Value Alignment
Teachers & Learners
Tech Developers
Academic researchers
ENABLER 3
Engage educators,
trainers, researchers and
AI developers in co-
development
ENABLER 1
Train educators
and trainers
ENABLER 2
Train AI
developers
Data,
evidence
and research
Cukurova, M., Luckin, R., & Clark-Wilson, A. (2019). Creating the golden triangle of evidence-informed education technology with EDUCATE. British Journal of Educational
Technology, 50(2), 490-504.
Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology,
50(6), 2824-2838. doi:10.1111/bjet.12861
Human-centred design for value alignment:
How Can We Design Analytics Interfaces Aligned to Teachers' Inquiry?
Pozdniakov, S., Martinez-Maldonado, R., Shan-Tsai, Y., Cukurova, M., Bartindale, T., Chen, P., Harrison, M., Richardson, D., & Gasevic, D. (2022). The Question-driven Dashboard: How Can We Design Analytics
Interfaces Aligned to Teachers' Inquiry?. Learning Analytics & Knowledge, ACM.
Human
Control
Automation through AI
Most traditional
Educational Technology
HA = Humans internalise MMLA
models H à A
Analytics and GOFAI: Changing
the operations and
representations of thought
H[A] = Human cognition (H)
extended with MMLA (A), tightly
coupled human and artificial
systems.
H[A] > (H) + (A)
AH = Human tasks are replaced
by MMLA H ß A
Initial vision of AIED: Early
Promises of Intelligent Tutoring
Systems
High
Low High
Low
2) Human-centred
3) Trustworthy
MMLA: A vision for the future
1) Learning sciences/
learning design-driven
Thank you
Dr. Mutlu Cukurova
m.cukurova@ucl.ac.uk
@mutlucukurova
PhD students and Collaborators:, Wannapon Suraworachet, Qi Zhou, Ali Chaudhry, Haifa Al-Wahaby, Rose Luckin, Carmel Kent, Benedict Du Boulay, Juan I.
Asensio-Pérez, Daniel Spikol, Manolis Mavrikis, Yannis Dimitriadis, Stanislav Pozdniakov, Roberto Martinez-Maldonado, Tom Bartindale, Peter Chen, Dan
Richardson, Fan Ouyang, Xi Wu, Michali Giannakos, Zacharoula Papamitsiou, Oya Celiktutan, Anjelika Kasparova, Cristina Villa-Torrano, Abayomi Akanji

More Related Content

What's hot

Skin lesion detection from dermoscopic images using Convolutional Neural Netw...
Skin lesion detection from dermoscopic images using Convolutional Neural Netw...Skin lesion detection from dermoscopic images using Convolutional Neural Netw...
Skin lesion detection from dermoscopic images using Convolutional Neural Netw...Adrià Romero López
 
T9. Trust and reputation in multi-agent systems
T9. Trust and reputation in multi-agent systemsT9. Trust and reputation in multi-agent systems
T9. Trust and reputation in multi-agent systemsEASSS 2012
 
Hierarchical clustering
Hierarchical clusteringHierarchical clustering
Hierarchical clusteringChakrit Phain
 
A survey of deep learning approaches to medical applications
A survey of deep learning approaches to medical applicationsA survey of deep learning approaches to medical applications
A survey of deep learning approaches to medical applicationsJoseph Paul Cohen PhD
 
Unit 1 - ML - Introduction to Machine Learning.pptx
Unit 1 - ML - Introduction to Machine Learning.pptxUnit 1 - ML - Introduction to Machine Learning.pptx
Unit 1 - ML - Introduction to Machine Learning.pptxjawad184956
 
Clustering paradigms and Partitioning Algorithms
Clustering paradigms and Partitioning AlgorithmsClustering paradigms and Partitioning Algorithms
Clustering paradigms and Partitioning AlgorithmsUmang MIshra
 
Fourier descriptors & moments
Fourier descriptors & momentsFourier descriptors & moments
Fourier descriptors & momentsrajisri2
 
Chapter 13. Trends and Research Frontiers in Data Mining.ppt
Chapter 13. Trends and Research Frontiers in Data Mining.pptChapter 13. Trends and Research Frontiers in Data Mining.ppt
Chapter 13. Trends and Research Frontiers in Data Mining.pptSubrata Kumer Paul
 
Image Segmentation using Otsu's Method - Computer Graphics (UCS505) Project PPT
Image Segmentation using Otsu's Method - Computer Graphics (UCS505) Project PPTImage Segmentation using Otsu's Method - Computer Graphics (UCS505) Project PPT
Image Segmentation using Otsu's Method - Computer Graphics (UCS505) Project PPTAkshit Arora
 
Cure, Clustering Algorithm
Cure, Clustering AlgorithmCure, Clustering Algorithm
Cure, Clustering AlgorithmLino Possamai
 
Introduction to Some Tree based Learning Method
Introduction to Some Tree based Learning MethodIntroduction to Some Tree based Learning Method
Introduction to Some Tree based Learning MethodHonglin Yu
 
CV_1 Introduction of Computer Vision and its Application
CV_1 Introduction of Computer Vision and its ApplicationCV_1 Introduction of Computer Vision and its Application
CV_1 Introduction of Computer Vision and its ApplicationKhushali Kathiriya
 
Feature Extraction
Feature ExtractionFeature Extraction
Feature Extractionskylian
 
Machine Learning - Object Detection and Classification
Machine Learning - Object Detection and ClassificationMachine Learning - Object Detection and Classification
Machine Learning - Object Detection and ClassificationVikas Jain
 
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
 
CLUSTERING IN DATA MINING.pdf
CLUSTERING IN DATA MINING.pdfCLUSTERING IN DATA MINING.pdf
CLUSTERING IN DATA MINING.pdfSowmyaJyothi3
 
Morphological Image Processing
Morphological Image ProcessingMorphological Image Processing
Morphological Image Processingkumari36
 

What's hot (20)

Skin lesion detection from dermoscopic images using Convolutional Neural Netw...
Skin lesion detection from dermoscopic images using Convolutional Neural Netw...Skin lesion detection from dermoscopic images using Convolutional Neural Netw...
Skin lesion detection from dermoscopic images using Convolutional Neural Netw...
 
T9. Trust and reputation in multi-agent systems
T9. Trust and reputation in multi-agent systemsT9. Trust and reputation in multi-agent systems
T9. Trust and reputation in multi-agent systems
 
Hierarchical clustering
Hierarchical clusteringHierarchical clustering
Hierarchical clustering
 
A survey of deep learning approaches to medical applications
A survey of deep learning approaches to medical applicationsA survey of deep learning approaches to medical applications
A survey of deep learning approaches to medical applications
 
Unit 1 - ML - Introduction to Machine Learning.pptx
Unit 1 - ML - Introduction to Machine Learning.pptxUnit 1 - ML - Introduction to Machine Learning.pptx
Unit 1 - ML - Introduction to Machine Learning.pptx
 
Scaling and Normalization
Scaling and NormalizationScaling and Normalization
Scaling and Normalization
 
Clustering paradigms and Partitioning Algorithms
Clustering paradigms and Partitioning AlgorithmsClustering paradigms and Partitioning Algorithms
Clustering paradigms and Partitioning Algorithms
 
Fourier descriptors & moments
Fourier descriptors & momentsFourier descriptors & moments
Fourier descriptors & moments
 
Chapter 13. Trends and Research Frontiers in Data Mining.ppt
Chapter 13. Trends and Research Frontiers in Data Mining.pptChapter 13. Trends and Research Frontiers in Data Mining.ppt
Chapter 13. Trends and Research Frontiers in Data Mining.ppt
 
Image Segmentation using Otsu's Method - Computer Graphics (UCS505) Project PPT
Image Segmentation using Otsu's Method - Computer Graphics (UCS505) Project PPTImage Segmentation using Otsu's Method - Computer Graphics (UCS505) Project PPT
Image Segmentation using Otsu's Method - Computer Graphics (UCS505) Project PPT
 
Cure, Clustering Algorithm
Cure, Clustering AlgorithmCure, Clustering Algorithm
Cure, Clustering Algorithm
 
Introduction to Some Tree based Learning Method
Introduction to Some Tree based Learning MethodIntroduction to Some Tree based Learning Method
Introduction to Some Tree based Learning Method
 
CV_1 Introduction of Computer Vision and its Application
CV_1 Introduction of Computer Vision and its ApplicationCV_1 Introduction of Computer Vision and its Application
CV_1 Introduction of Computer Vision and its Application
 
Feature Extraction
Feature ExtractionFeature Extraction
Feature Extraction
 
Machine Learning - Object Detection and Classification
Machine Learning - Object Detection and ClassificationMachine Learning - Object Detection and Classification
Machine Learning - Object Detection and Classification
 
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...
 
Data mining primitives
Data mining primitivesData mining primitives
Data mining primitives
 
CLUSTERING IN DATA MINING.pdf
CLUSTERING IN DATA MINING.pdfCLUSTERING IN DATA MINING.pdf
CLUSTERING IN DATA MINING.pdf
 
Morphological Image Processing
Morphological Image ProcessingMorphological Image Processing
Morphological Image Processing
 
DDBMS Paper with Solution
DDBMS Paper with SolutionDDBMS Paper with Solution
DDBMS Paper with Solution
 

Similar to 2022_11_11 «The promise and challenges of Multimodal Learning Analytics»

yannis@its2022_20220701_final.pptx
yannis@its2022_20220701_final.pptxyannis@its2022_20220701_final.pptx
yannis@its2022_20220701_final.pptxYannis
 
yannis@gu_20221027.pptx
yannis@gu_20221027.pptxyannis@gu_20221027.pptx
yannis@gu_20221027.pptxYannis
 
Digitaaliset välineet opetuksessa ja oppimisessa opettajankoulutuksen konteks...
Digitaaliset välineet opetuksessa ja oppimisessa opettajankoulutuksen konteks...Digitaaliset välineet opetuksessa ja oppimisessa opettajankoulutuksen konteks...
Digitaaliset välineet opetuksessa ja oppimisessa opettajankoulutuksen konteks...Jari Laru
 
Are we currently moving from the age of mobolism to age of artificail intelli...
Are we currently moving from the age of mobolism to age of artificail intelli...Are we currently moving from the age of mobolism to age of artificail intelli...
Are we currently moving from the age of mobolism to age of artificail intelli...Jari Laru
 
Assessment for e-learning and online learning
Assessment for e-learning and online learningAssessment for e-learning and online learning
Assessment for e-learning and online learningmelanieroul6610
 
Learning analytics dashboards
Learning analytics dashboardsLearning analytics dashboards
Learning analytics dashboardsKatrien Verbert
 
A content analysis of the emerging research on academic cyberloafing
A content analysis of the emerging research on academic cyberloafingA content analysis of the emerging research on academic cyberloafing
A content analysis of the emerging research on academic cyberloafingZizo Aku
 
Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insigh...
Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insigh...Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insigh...
Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insigh...Talis
 
PREDICTING FUTURE OF EDTECH 2030 v2
PREDICTING FUTURE OF EDTECH 2030 v2PREDICTING FUTURE OF EDTECH 2030 v2
PREDICTING FUTURE OF EDTECH 2030 v2Jari Laru
 
Human-Centered Learning Analytics and Artificial Intelligence in Education: H...
Human-Centered Learning Analytics and Artificial Intelligence in Education: H...Human-Centered Learning Analytics and Artificial Intelligence in Education: H...
Human-Centered Learning Analytics and Artificial Intelligence in Education: H...Yannis
 
Group 2 fdi presentation
Group 2 fdi presentationGroup 2 fdi presentation
Group 2 fdi presentationTony Reeves
 
Yhteisöllisen oppimisen tukeminen mobiililaitteiden avulla: Kolme tapaustutki...
Yhteisöllisen oppimisen tukeminen mobiililaitteiden avulla: Kolme tapaustutki...Yhteisöllisen oppimisen tukeminen mobiililaitteiden avulla: Kolme tapaustutki...
Yhteisöllisen oppimisen tukeminen mobiililaitteiden avulla: Kolme tapaustutki...Jari Laru
 
Towards Collaborative Learning Analytics
Towards Collaborative Learning AnalyticsTowards Collaborative Learning Analytics
Towards Collaborative Learning Analyticsalywise
 
Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...
Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...
Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...Bart Rienties
 
Cognitive Computing and Education and Learning
Cognitive Computing and Education and LearningCognitive Computing and Education and Learning
Cognitive Computing and Education and Learningijtsrd
 
Artificial Intelligence and Data Analytics in Education: the case of explorat...
Artificial Intelligence and Data Analytics in Education: the case of explorat...Artificial Intelligence and Data Analytics in Education: the case of explorat...
Artificial Intelligence and Data Analytics in Education: the case of explorat...Manolis Mavrikis
 
Serious Games & Immersive Technologies for Learning: possibilities & perspect...
Serious Games & Immersive Technologies for Learning: possibilities & perspect...Serious Games & Immersive Technologies for Learning: possibilities & perspect...
Serious Games & Immersive Technologies for Learning: possibilities & perspect...Leonel Morgado
 
Learning Analytics and libraries
Learning Analytics and librariesLearning Analytics and libraries
Learning Analytics and librariesTalis
 
From mirroring to guiding
From mirroring to guidingFrom mirroring to guiding
From mirroring to guidingHarlyson Silva
 

Similar to 2022_11_11 «The promise and challenges of Multimodal Learning Analytics» (20)

yannis@its2022_20220701_final.pptx
yannis@its2022_20220701_final.pptxyannis@its2022_20220701_final.pptx
yannis@its2022_20220701_final.pptx
 
yannis@gu_20221027.pptx
yannis@gu_20221027.pptxyannis@gu_20221027.pptx
yannis@gu_20221027.pptx
 
Digitaaliset välineet opetuksessa ja oppimisessa opettajankoulutuksen konteks...
Digitaaliset välineet opetuksessa ja oppimisessa opettajankoulutuksen konteks...Digitaaliset välineet opetuksessa ja oppimisessa opettajankoulutuksen konteks...
Digitaaliset välineet opetuksessa ja oppimisessa opettajankoulutuksen konteks...
 
Are we currently moving from the age of mobolism to age of artificail intelli...
Are we currently moving from the age of mobolism to age of artificail intelli...Are we currently moving from the age of mobolism to age of artificail intelli...
Are we currently moving from the age of mobolism to age of artificail intelli...
 
Assessment for e-learning and online learning
Assessment for e-learning and online learningAssessment for e-learning and online learning
Assessment for e-learning and online learning
 
Learning analytics dashboards
Learning analytics dashboardsLearning analytics dashboards
Learning analytics dashboards
 
A content analysis of the emerging research on academic cyberloafing
A content analysis of the emerging research on academic cyberloafingA content analysis of the emerging research on academic cyberloafing
A content analysis of the emerging research on academic cyberloafing
 
Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insigh...
Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insigh...Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insigh...
Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insigh...
 
PREDICTING FUTURE OF EDTECH 2030 v2
PREDICTING FUTURE OF EDTECH 2030 v2PREDICTING FUTURE OF EDTECH 2030 v2
PREDICTING FUTURE OF EDTECH 2030 v2
 
Human-Centered Learning Analytics and Artificial Intelligence in Education: H...
Human-Centered Learning Analytics and Artificial Intelligence in Education: H...Human-Centered Learning Analytics and Artificial Intelligence in Education: H...
Human-Centered Learning Analytics and Artificial Intelligence in Education: H...
 
Group 2 fdi presentation
Group 2 fdi presentationGroup 2 fdi presentation
Group 2 fdi presentation
 
Yhteisöllisen oppimisen tukeminen mobiililaitteiden avulla: Kolme tapaustutki...
Yhteisöllisen oppimisen tukeminen mobiililaitteiden avulla: Kolme tapaustutki...Yhteisöllisen oppimisen tukeminen mobiililaitteiden avulla: Kolme tapaustutki...
Yhteisöllisen oppimisen tukeminen mobiililaitteiden avulla: Kolme tapaustutki...
 
Towards Collaborative Learning Analytics
Towards Collaborative Learning AnalyticsTowards Collaborative Learning Analytics
Towards Collaborative Learning Analytics
 
Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...
Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...
Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...
 
Cognitive Computing and Education and Learning
Cognitive Computing and Education and LearningCognitive Computing and Education and Learning
Cognitive Computing and Education and Learning
 
Artificial Intelligence and Data Analytics in Education: the case of explorat...
Artificial Intelligence and Data Analytics in Education: the case of explorat...Artificial Intelligence and Data Analytics in Education: the case of explorat...
Artificial Intelligence and Data Analytics in Education: the case of explorat...
 
Serious Games & Immersive Technologies for Learning: possibilities & perspect...
Serious Games & Immersive Technologies for Learning: possibilities & perspect...Serious Games & Immersive Technologies for Learning: possibilities & perspect...
Serious Games & Immersive Technologies for Learning: possibilities & perspect...
 
Learning Analytics and libraries
Learning Analytics and librariesLearning Analytics and libraries
Learning Analytics and libraries
 
McKay Publications 2015
McKay Publications 2015McKay Publications 2015
McKay Publications 2015
 
From mirroring to guiding
From mirroring to guidingFrom mirroring to guiding
From mirroring to guiding
 

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 «Biometrics and Behavior Understanding Technologies for e-Learning...
2022_11_11 «Biometrics and Behavior Understanding Technologies for e-Learning...2022_11_11 «Biometrics and Behavior Understanding Technologies for e-Learning...
2022_11_11 «Biometrics and Behavior Understanding Technologies for e-Learning...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 «Biometrics and Behavior Understanding Technologies for e-Learning...
2022_11_11 «Biometrics and Behavior Understanding Technologies for e-Learning...2022_11_11 «Biometrics and Behavior Understanding Technologies for e-Learning...
2022_11_11 «Biometrics and Behavior Understanding Technologies for e-Learning...
 

Recently uploaded

A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyUXDXConf
 
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomSalesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomCzechDreamin
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераMark Opanasiuk
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityScyllaDB
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?Mark Billinghurst
 
IESVE for Early Stage Design and Planning
IESVE for Early Stage Design and PlanningIESVE for Early Stage Design and Planning
IESVE for Early Stage Design and PlanningIES VE
 
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)Julian Hyde
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfFIDO Alliance
 
Speed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in MinutesSpeed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in Minutesconfluent
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessUXDXConf
 
Buy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptxBuy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptxEasyPrinterHelp
 
ECS 2024 Teams Premium - Pretty Secure
ECS 2024   Teams Premium - Pretty SecureECS 2024   Teams Premium - Pretty Secure
ECS 2024 Teams Premium - Pretty SecureFemke de Vroome
 
Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024TopCSSGallery
 
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...FIDO Alliance
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyJohn Staveley
 
Syngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon
 
Strategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsStrategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsUXDXConf
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101vincent683379
 
Introduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG EvaluationIntroduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG EvaluationZilliz
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...CzechDreamin
 

Recently uploaded (20)

A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System Strategy
 
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomSalesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджера
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through Observability
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?
 
IESVE for Early Stage Design and Planning
IESVE for Early Stage Design and PlanningIESVE for Early Stage Design and Planning
IESVE for Early Stage Design and Planning
 
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
 
Speed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in MinutesSpeed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in Minutes
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for Success
 
Buy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptxBuy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptx
 
ECS 2024 Teams Premium - Pretty Secure
ECS 2024   Teams Premium - Pretty SecureECS 2024   Teams Premium - Pretty Secure
ECS 2024 Teams Premium - Pretty Secure
 
Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024
 
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John Staveley
 
Syngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdf
 
Strategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsStrategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering Teams
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101
 
Introduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG EvaluationIntroduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG Evaluation
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
 

2022_11_11 «The promise and challenges of Multimodal Learning Analytics»

  • 1. Dr. Mutlu Cukurova University College London m.cukurova@ucl.ac.uk @mutlucukurova The Promise and Challenges of Multimodal Learning Analytics
  • 2. Three conceptualisations of MMLA 1. MMLA to Automate Human Tasks 2. MMLA to Augment Teaching and Learning Practices 3. MMLA as a Research Methodology
  • 3. “tools that have been designed and developed to replace human tasks through analysis of multiple modalities of data, and prediction of the best value for a designated outcome variable, which is conveyed through a user interface.” Multimodal Learning Analytics as:
  • 4. What Modalities of Data We Use? 0 5 10 15 20 25 30 35 40 Presenter’s pose, gaze direction, visual attention Audio, dialogue’s characteristics Facialdata and emotions Posture and gesture, body movement, distance, motion, Position Learners’ concentration level, visual attention and habits Learners’ gaze in off- screen activity Student’s physiological data, arousal and EDA Students’ cognitive load Posture and gesture Location, position and duration, movement Sitting position EEG data, brain activity Video and audio Eye-tracking Skin sensing In depth camera Location sensing Pressure sensing EEG sensor Number of published articles 2010 - 2020 Alwahaby, H., Cukurova, M., Papamitsiou, Z., & Giannakos, M. (2021). The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review. https://doi.org/10.35542/osf.io/sd23y
  • 5. MMLA as Machine Learning Tools Human Control Automation through AI Most traditional Educational Technology AH = Human tasks are replaced by MMLA H ß A Initial vision of AIED: Early Promises of Intelligent Tutoring Systems High Low High Low
  • 6.
  • 7. Machine Learning Classification of CPS Competence Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018). Supervised machine learning in multimodal learning analytics for estimating success in project-based learning. Journal of Computer Assisted Learning, 34(4), 366-377.
  • 8. Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018). Supervised machine learning in multimodal learning analytics for estimating success in project-based learning. Journal of Computer Assisted Learning, 34(4), 366-377. The promise of MMLA
  • 9. R. Kawamura et al., Detecting Drowsy Learners at the Wheel of e-Learning Platforms With Multimodal Learning Analytics, IEEE Access, vol. 9, pp. 115165-115174, 2021, doi: 10.1109/ACCESS.2021.3104805
  • 10. “to support learning experiences that may be collaborative, hands-on, and face-to-face, de-emphasizing the computer screen as the primary form or object of interaction.” Multimodal Learning Analytics: Worsley, M., Martinez-Maldonado, R., & D'Angelo, C. (2021). "A New Era in Multimodal Learning Analytics: Twelve Core Commitments to Ground and Grow MMLA." Journal of Learning Analytics 8.3, 10-27. 2. MMLA to Augment Teaching and Learning Practices
  • 11. MMLA to Augment Teaching and Learning Practices Human Control Automation through AI Most traditional Educational Technology HA = Humans internalise MMLA models H à A Analytics and GOFAI: Changing the operations and representations of thought AH = Human tasks are replaced by AI H ß A Initial vision of AIED: Early Promises of Intelligent Tutoring Systems High Low High Low
  • 12. 17 Cukurova, M., Kent, C., & Luckin, R. (2019). Artificial Intelligence and Multimodal Data in the Service of Human Decision-making: A case Study in Debate Tutoring, British Journal of Educational Technology, 50 (6), 3032-3046.
  • 13. Models as learning affordances for humans Kent, C., Chaudhry, M., Cukurova, M. Bashir, I., Pickard, H., Jenkins, C., du Boulay, B., Luckin, R., (2021). Machine learning models and their development process as learning affordances for humans. International Conference of Artificial Intelligence in Education, Springer, Cham. What do successful students look like? What are their behaviour and personality traits? Mental model : diagram of functionality Initial student profiles
  • 14. Active Active Passive Kasparova, A., Celiktutan, O., & Cukurova, M. (2020). Inferring Student Engagement in Collaborative Problem Solving from Visual Cues. Companion Publication of the 2020 International Conference on Multimodal Interaction. https://doi.org/10.1145/3395035.3425961
  • 15. From machine observations to learning sciences constructs of collaboration Interpretationsof Collaboration Competence from Ma Observables Cukurova, M., Luckin, R., Millán, E., & Mavrikis, M. (2018). The NISPI framework: Analysing collaborative problem-solvin students' physical interactions. Computers & Education, 116, 93-109. Some important components of collaboration may be interpreted through automated machine observable data: • synchrony, • individual accountability, • equality, • mutuality. Cukurova, M., Luckin, R., Millán, E., & Mavrikis, M. (2018). The NISPI framework: Analysing collaborative problem-solving from students' physical interactions. Computers & Education, 116, 93-109. Cukurova, M. (2018). A syllogism for designing collaborative learning technologies in the age of AI and multimodal data. In European Conference on Technology Enhanced Learning (pp. 291-296). Springer, Cham.
  • 16. Cukurova, M., Luckin, R., Millán, E., & Mavrikis, M. (2018). The NISPI framework: Analysing collaborative problem-solving from students' physical interactions. Computers & Education, 116, 93-109. 14
  • 17. Cukurova, M., Mavrikis, M., & Luckin, R. (2017). Evidence-Centered Design and Its Application to Collaborative Problem Solving in Practice-based Learning Environments. Analytics4Learning. Stanford Research Institute, Menlo Park, USA.
  • 18. Learning sciences should drive the features. Learner/Teachers’ needs and expectations should be met. Kent, C., & Cukurova, M. (2020). Investigating Collaboration as a Process with Theory-driven Learning Analytics. Journal of Learning Analytics, 7(1), 59-71. Cukurova, M., Kent, C., & Akanji, A. (2022). Identifying tertiary level educators' needs and understanding of the collaboration process analytics. https://doi.org/10.35542/osf.io/2d3ty
  • 19. MMLA as a Research Methodology 3. MMLA as a Research Methodology
  • 20. Ouyang, F., Xu, W., Cukurova, M. (2022). An Artificial Intelligence-driven Learning Analytics Method to Examine the Collaborative Problem-solving Process from the Complex Adaptive Systems Perspective, International Journal of Computer Supported Collaborative Learning. http://arxiv.org/abs/2210.16059v1 Interactive Peer interaction through communications (Int-C) Peer interaction through behaviours (Int-B) Cognitive Superficial-level knowledge (KS) Medium-level knowledge (KM) Deep-level knowledge (KD) Behavioural Resource management (RM) Concept mapping (CM) Observation (OB) Regulative Task understanding (TU) Goal setting and planning (GSP) Monitoring and reflection (MR) Socio-emotional Active listening and respect (ALR) Encouraging participation and inclusion (EPI) Fostering cohesion (FC)
  • 21. • Behaviour-oriented collaborative pattern (Type 1) - Medium-level performance • Communication-behaviour-synergistic collaborative pattern (Type 2) – High-level performance • Communication-oriented collaborative pattern (Type 3) – Low-level performance Ouyang, F., Xu, W., Cukurova, M. (2022). An Artificial Intelligence-driven Learning Analytics Method to Examine the Collaborative Problem-solving Process from the Complex Adaptive Systems Perspective, International Journal of Computer Supported Collaborative Learning. http://arxiv.org/abs/2210.16059v1
  • 22.
  • 23. • Peer-interaction focussed collaborative pattern (PIF Type) • Resources-interaction focusses collaborative pattern (RIF Type) Zhou, Q., Suraworachet, W., Celiktutan, O., & Cukurova, M. (2022). What Does Shared Understanding in Students’ Face-to-Face Collaborative Learning Gaze Behaviours “Look Like”?. In International Conference on Artificial Intelligence in Education (pp. 588-593). Springer, Cham.
  • 24. 1. Methodological - ML AI is not that intelligent - AIP 2. Logistical/practical/financial 3. Connection to learning theory and learning design Short-term Challenges of MMLA Cukurova, M., Giannakos, M., & Martinez-Maldonado, R. (2020). The Promise and Challenges of Multimodal Learning Analytics. British Journal of Educational Technology, 51(5), pp. 1441-1449, https://doi.org/10.1111/bjet.13015. Zhou, Q., Suraworachet, W., Cukurova, M. (2021). Different modality, different design, different results: exploring self-regulated learner clusters’ engagement behaviours at individual, group and cohort activities. https://doi.org/10.35542/osf.io/u3g4n
  • 25. “More Significant” Challenges of MMLA – Particularly for the first conceptualisation Normativity 1. What behaviours are good/bad in education? 2. Inference based on history 3. Flourishing Diversity/ in uncertainty Prediction 1. What is it good for/intervention? 2. Fairness 3. Transparency 4. Accountability Human Agency 1. Balancing exploration and exploitation 2. The reward function 3. Privacy/ surveillance Ethics- Human Values Alwahaby, H., Cukurova, M., Papamitsiou, Z., & Giannakos, M. (2022). The Evidence of Impact and Ethical Considerations of Multimodal Learning Analytics: A Systematic Literature Review. In The Multimodal Learning Analytics Handbook (pp. 289-325). Springer International Publishing. doi:10.1007/978-3-031-08076-0_12
  • 26. Students have mixed reactions to MMLA Zhou,Q., Suraworachet, W., Pozdniakov, S., Martinez-Maldonado, R., Bartindale, T., Chen, P. Richardson, D., & Cukurova M. (2021). Investigating Students’ Experiences with Collaboration Analytics for Remote Group Meetings. International Conference of Artificial Intelligence in Education, Springer, Cham. Zhou, Q., Suraworachet, W., Cukurova, M. (2021). Different modality, different design, different results: exploring self-regulated learner clusters’ engagement behaviours at individual, group and cohort activities. https://doi.org/10.35542/osf.io/u3g4n - Were not concerned (or faded) due to: the module domain, invisibility, not parts of sum assessment - More concerning for low contributors - Motivational for increased accountability for high contributors
  • 27. Nazaretsky, T., Cukurova, M., Ariely, M., & Alexandron, G. (2021). Confirmation bias and trust: Human factors that influence teachers' attitudes towards AI-based educational technology, CEUR Workshop Proceedings (Vol. 3042). Cukurova, M., Luckin, R., & Kent, C. (2019). Impact of an Artificial Intelligence Research Frame on the Perceived Credibility of Educational Research Evidence. International Journal of Artificial Intelligence in Education, 1-31. Teachers have confirmation bias and unrealistic expectations from AI-based EdTech. "AI framing effect": when people (including teachers) are presented with research evidence framed as coming from AIED, they tend to judge it as less credible compared to educational psychology and neuroscience. Teachers appear to be more critical
  • 28. How do we gain teachers’ trust in MMLA? • Developed a survey instrument that revealed eight dimensions. Alwahaby, H., Cukurova, M., Papamitsiou, Z., & Giannakos, M. (2021). The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review. https://doi.org/10.35542/osf.io/sd23y Nazaretsky, T., Cukurova, M., Alexandron, G. (2022). An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology. Learning Analytics and Knowledge Conference. ACM: New York. 1. Perceived Benefits 2. Lack of Human Characteristics 3. Lack of Transparency 4. Anxieties 5. Self-efficacy 6. Required Shift in Pedagogy 7. Means to Increase Trust 8. AI-EdTech vs Human Advice Strongly not agree Not agree Strongly agree Agree Neutral
  • 29. How would a CPD to increase teachers’ trust in intelligent technologies look like? Nazaretsky, T., Ariely, M., Cukurova, M., Alexandron, G. (2022). Teachers’ Trust in AI-powered Educational Technology and a Professional Development Program to Improve It, British Journal of Educational Technology, DOI: 10.1111/bjet.13232
  • 30. What do we mean by transparency? Transparency for whom? Chaudhry, M. A., Cukurova, M., & Luckin, R. (2022). A Transparency Index Framework for AI in Education. Artificial Intelligence in Education. https://arxiv.org/abs/2206.03220 .
  • 31. Cukurova, M., Zhou, Q., Spikol, D., & Landolfi, L. (2020). Modelling collaborative problem-solving competence with transparent learning analytics: is video data enough?. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 270-275). Listening Speaking Transparent decision trees to model Collaborative problem solving competence Watching Making Collaborative problem-solving competence
  • 32. The Golden Triangle for Value Alignment Teachers & Learners Tech Developers Academic researchers ENABLER 3 Engage educators, trainers, researchers and AI developers in co- development ENABLER 1 Train educators and trainers ENABLER 2 Train AI developers Data, evidence and research Cukurova, M., Luckin, R., & Clark-Wilson, A. (2019). Creating the golden triangle of evidence-informed education technology with EDUCATE. British Journal of Educational Technology, 50(2), 490-504. Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824-2838. doi:10.1111/bjet.12861
  • 33. Human-centred design for value alignment: How Can We Design Analytics Interfaces Aligned to Teachers' Inquiry? Pozdniakov, S., Martinez-Maldonado, R., Shan-Tsai, Y., Cukurova, M., Bartindale, T., Chen, P., Harrison, M., Richardson, D., & Gasevic, D. (2022). The Question-driven Dashboard: How Can We Design Analytics Interfaces Aligned to Teachers' Inquiry?. Learning Analytics & Knowledge, ACM.
  • 34. Human Control Automation through AI Most traditional Educational Technology HA = Humans internalise MMLA models H à A Analytics and GOFAI: Changing the operations and representations of thought H[A] = Human cognition (H) extended with MMLA (A), tightly coupled human and artificial systems. H[A] > (H) + (A) AH = Human tasks are replaced by MMLA H ß A Initial vision of AIED: Early Promises of Intelligent Tutoring Systems High Low High Low 2) Human-centred 3) Trustworthy MMLA: A vision for the future 1) Learning sciences/ learning design-driven
  • 35. Thank you Dr. Mutlu Cukurova m.cukurova@ucl.ac.uk @mutlucukurova PhD students and Collaborators:, Wannapon Suraworachet, Qi Zhou, Ali Chaudhry, Haifa Al-Wahaby, Rose Luckin, Carmel Kent, Benedict Du Boulay, Juan I. Asensio-Pérez, Daniel Spikol, Manolis Mavrikis, Yannis Dimitriadis, Stanislav Pozdniakov, Roberto Martinez-Maldonado, Tom Bartindale, Peter Chen, Dan Richardson, Fan Ouyang, Xi Wu, Michali Giannakos, Zacharoula Papamitsiou, Oya Celiktutan, Anjelika Kasparova, Cristina Villa-Torrano, Abayomi Akanji