Machine Learning-Driven Digital Technologies for Education Innovation
The Machine Learning-Driven Digital Technologies for Educational Innovation Workshop was the first edition of the Future of Educational Innovation Workshop Series produced by the Institute for the Future of Education of Tecnologico de Monterrey. The workshop was organized during the COVID-19 pandemic. Given that most countries had very active vaccination programs, we initially envisioned the workshop to be an in-person event in Monterrey, Mexico, home to the main campus of Tecnologico de Monterrey. However, international travel restrictions and the imminent arrival of the Delta variant of SARS-CoV-2 in our country compelled us to switch to an online version. This meant migrating all the operations to an online venue and coordinating with authors in just weeks. The organizing committee and staff rose to the challenge, communicating with and organizing all the stakeholders: keynote speakers, participants, and technical session moderators.
The core purpose of this first edition of the workshop series was to present state-of-the-art research to expand the understanding of how machine learning is shaping innovation and emerging technologies in education. One of the main objectives was to show how researchers in the various educational fields are innovating and using machine learning in pedagogical approaches and ideas. We aimed to examine how technology helps researchers understand educational phenomena from a data science perspective. By bringing together researchers with unique perspectives in these fields, we provided a space for them to present new concepts and viewpoints that can shape the future of education and educational practices designed and implemented using new research paradigms.
The main organizer of the workshop was the Writing Lab of the Institute for the Future of Education from Tecnologico de Monterrey. It was technically co-sponsored by the IEEE Education Society, IEEE Region 9, IEEE, and Tecnologico de Monterrey. The workshop program included two days of lectures and presentation sessions.
The main lectures titled “Fundamentals of Machine Learning” and “Machine Learning in the Development of Educational Technology” were presented by Prof. Amlan Chakrabarti (University of Calcutta, India) and Dr. Amit Kumar Das (University of Calcutta, India). The main topics covered were the definition, applications, and forms of Machine Learning. The participants also had the opportunity to work on two case studies to put into practice the theory.
The call for papers welcomed research and practice-based work in the area. We would like to thank all the contributing authors. We appreciate the excellent contributions of our reviewers to ensure high-quality workshop proceedings. The editorial committee accepted 33 articles for publication in the proceedings, now available in the IEEE Xplore.