Future of Educational Innovation-Workshop series
Machine Learning-Driven Digital Technologies for Educational Innovation
15 – 17 December, 2021
Monterrey, Mexico
In Partnership with
Machine learning is a popular field of computer science that relies on statistical techniques and algorithms to classify, order and interpret data, identify patterns and make decisions with minimal human intervention. The emergence of machine learning algorithms, big data calculations and immediate response-action as discerned from the machine-learning process are presenting new and exciting possibilities to Education. Its ability to provide real-time learning has facilitated its popularity within an education sector which, having embraced the uses of the digital technology, has seen the need for innovative educational.
The Workshop on Application of Machine Learning to Educational Innovation: Trends and Challenges aims at presenting current research to expand our understanding of how machine learning is shaping innovation and emerging technologies in Education. The focus of this workshop is presenting how researchers in the fields of Education are innovating and examining machine learning to study pedagogical approaches and ideas. We would like to examine how this technology may help researchers better understand Educational phenomena from a perspective of data Sciences.
By bringing together researchers with unique perspectives in these fields, we hope to provide a space for presenters not only to present new ideas and perspectives that are shaping the future of Education but also how Educational practice is being shaped by new research paradigms.
Program
Day 1
15 December
- Opening statement (09:30 am)
- Lectures (10:00 am – 01:00 pm)
Fundamentals of Machine Learning (Presenters: Prof. Amlan Chakrabarti and Dr. Amit Das) Proposed topics to be covered:
What is machine learning (ML)?
Different forms of ML
Process of ML model training, prediction and evaluation
Where to apply ML and where not to
Few popular applications of ML
- Lunch break (01:00pm – 03:00 pm)
- Presentations of research (03:00 pm – 05:00 pm)
3 parallel tracks – 8 papers each track
Track 1: Digital Technologies for Educational Innovation
Track 2: Machine Learning in Educational Innovation
Track 3: Inclusion of Digital Technologies during COVID-19
Day 2
16 December
- Lectures (10:00 am – 01:00 pm)
Machine Learning in Development of Educational Technology 1 and 2 (Presenters: Prof. Amlan Chakrabarti and Dr. Amit Das)
Proposed topics to be covered:
Frontiers of Applying ML in Digitizing Education
Case study 1 – ML based prediction of Student Grades
Case study 2 – ML based prediction of Student Employability
- Lunch break (01:00pm – 03:00 pm)
Presentations of research (03:00 pm – 05:00 pm)
3 parallel tracks – 8 papers each track
Track 4: Virtual and Augmented reality in education
Track 5: Reimaging education and Educational Technologies
Track 6: Data Science Driven Educational Practices
- Closing statement (05:00 pm)
Invited
Presenters
Professor Amlan Chakrabarti
Amlan Chakrabarti is a Full Professor of Information Technology in the A.K.Choudhury School of Information Technology at the University of Calcutta. He is also the former Dean, Faculty of Engineering and Technology of his university. He was a Post-Doctoral fellow at the School of Engineering, Princeton University, USA during 2011-2012. He is the recipient of DST BOYSCAST fellowship award in Engineering Science in 2011, Indian National Science Academy (INSA) Visiting Faculty Fellowship in 2014, JSPS Invitation Research Award in 2016 and Erasmus Mundus Leaders Award from EU in 2017, Hamied Visiting Lecture from the University of Cambridge, United Kingdom in 2018 and the Siksha Ratna Award, from the Department of Higher Education, Western Bengal, India, 2018. He has been associated in various capacities in numerous organizations of higher education both nationally and internationally. He has published around 150 research articles in referred journals and conferences. He is associate editor in Elsevier’s Journal of Computer and Electrical Engineering, editor of the Springer series Transactions on computer Systems and Networks, and guest editor of Springer’s Journal of Applied Sciences. He is a Sr. Member of IEEE and ACM, distinguished guest of IEEE Computer Society, distinguished speaker at ACM, Secretary of IEEE CEDA India Chapter and vice-president of Data Science Society. His research interests are Machine Learning, Computer Vision, Reconfigurable Computing, Cyberphysical Systems, VLSI CAD and Quantic Computing.
Professor Amit Kumar Das
Dr. Amit Kumar Das Amit is a seasoned industry practitioner turned to a full-time academician. He is currently working as an Assistant Professor, Institute of Engineering & Management. He is also a Senior Research Scientist in the Data Science Lab, A. K. Choudhury School of IT, University of Calcutta. He is also involved in industry consulting in the area of Data Science. Amit is a Senior Member, IEEE. Before joining academics, he has spent 18 years in the IT industry. Amit has authored many journal, conference articles and book chapters. He is also a reviewer in multiple journals (SCI / SCIE and IEEE Transactions). He has written three text books for the under-graduate students – on Machine Learning, Deep Learning and Big Data published by Pearson. He has been a regular speaker in the area of data analytics and machine learning.
Call for Papers
Call for Papers and deadlines Home Program Call for Papers and deadlines Organizers Registration Proceedings Contact Us Workshop topics This workshop welcomes research, and practice-based work in the area. We encourage authors to present their research results or work in progress that address the previous questions but with an emphasis on presenting machine learning applications in the following topics:
- Educational Data Mining and Learning Analytics
- Technology-enhanced learning
- Novel approaches for transforming educational environments
- Human–Machine Collaborative Learning
- Curriculum adaptation to prepare workforce for Industry 4.0
- Data-driven Education
- Digitization and Artificial Intelligence
- Immersive Learning Environments
- Ubiquitous smart technologies in education
- Virtual, Augmented and Mixed Reality Learning Environments
- Wearable and Mobile Learning
- AI enabled Active Learning
- Robotics in Education
The accepted papers will be submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements. The most promising contributions presented at this workshop will be invited to send their extended versions to the linked Special Issues.
We are calling for papers addressing (but not limited to) one or several of the following questions:
- What are the challenges facing educational researchers employing machine learning as a research tool?
- What strategies can be implemented in real pedagogical contexts combining machine learning applications in educationally innovative contexts?
- How are these methodologies, tools and technologies being blended into existing practices?
- What are the new data points from emerging technologies in machine learning settings?
- How are the results of machine learning approaches supporting different educational domains?
- What are the ethical considerations when employing machine learning to support educational innovations?
- What is the future of machine learning to support educational innovations?
- What role can AI and ML play in facilitating home-based learning in a pandemic situation?
- How can ML enable evaluating learning efficiency of students using streaming video inputs?
- How can ML facilitate academic evaluation for students to gauge teaching-learning effectiveness?
Dates of importance
Submission deadline (Extended Abstract):
8 August 2021 – 22 August 2021
Acceptance Notification:
9 September 2021
Early bird registration:
13 October 2021
Final registration:
31 October 2021
Full paper submission:
15 December 2021
Workshop participation and presentation:
15-17 December 2021
Revision Notification:
15 January 2022
Full paper acceptance notification:
6 February 2022
Submission process
You may submit your contribution in two PDF documents: a blind extended abstract (without author(s) information) and a document with the abstract title and author(s) information.
It is necessary to use the IEEE template for the extended abstract.
Publications
The accepted papers will be submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements. The most promising contributions presented at this workshop will be invited to send their extended versions to the linked Special Issues, including:
Equity and justice in health: the way forward in lifestyle medicine for Latin America
Journal: Ciência & Saúde Coletiva
Website: http://www.cienciaesaudecoletiva.com.br/
Guest editorial Team:
- Ross Arena PhD, PT, FAHA, FESC, University of Illinois, Chicago, United States
- Daniela Bassi Dibai PhD, PT, MSc, Ceuma University, Brazil
- Samira Hosseini (PhD), Writing Lab, Tecnológico de Monterrey, Mexico
- Asad Abbas (PhD), Tecnológico de Monterrey, Mexico
- Mildred Lopez PhD, MSc, AFAMEE, Tecnológico de Monterrey, Mexico.
Applications and Development in Linked Open Data (LOD) Cloud
Journal: Journal of Big Data
Website: https://journalofbigdata.springeropen.com
Guest editorial Team:
- Kingsley Okoye (PhD), Tecnologico de Monterrey, Mexico
- Samira Hosseini (PhD), Writing Lab, Tecnológico de Monterrey, Mexico
- Amlan Chakrabarti (PhD), University of Calcutta, India
- Julius T. Nganji (PhD), University of Toronto, Canada
Emerging Technologies in Education for Innovative Pedagogies and
Competency Development
Journal: Australasian Journal of Educational Technology
Website: https://ajet.org.au/index.php/AJET/SpecialIssueCall
Guest editorial Team:
- Asad Abbas (PhD), Tecnológico de Monterrey, Mexico
- Samira Hosseini (PhD), Writing Lab, Tecnológico de Monterrey, Mexico
- José Luis Martín Núñez (PhD), Universidad Politécnica de Madrid, Spain
- Susana Sastre-Merion (PhD), Universidad Politécnica de Madrid, Spain.
Organizing Committee
General chairs:
Prof. Samira Hosseini
Tecnologico de Monterrey, Mexico
Prof. Amlan Chakrabarti
University of Calcutta, India
General Coordination
Dr. Esmeralda Campos
Managing Editor
Sofía Reveles
Operations Coordinator
Technical Program Committee
Dr. Asad Abbas
Program Chair
Dr. Kingsley Okoye
Program co-chair
Dr. Ivan Acebo
Organizer
Dr. Genaro Rebolledo
Program Chair
Dr. Mildred Lopez
Program co-chair
Registrations
Two-day workshop Participation
Two-day workshop Participation
Certification
Access to the online writing tools and trainings
Early Bird (October 13)
$388 USD
Final registration (October 31st)
$440 USD
Early Bird (October 13)
$500 USD
Final registration (October 31st)
$560 USD
Disclaimer : This event was an independent university initiative, not affiliated with SAMYRAD.
Sponsors
