Workshop 43: Adopting the Internet of Educational Things (IoETs)Title: Computational, A.I and Machine Learning Models for Resolving Challenges with Internet of Educational Things
Workshop 43: Adopting the Internet of Educational Things (IoETs)
Title: Computational, A.I and Machine Learning Models for Resolving Challenges with Internet of Educational Things
Keywords: Internet-of-Educational-Things, Models, Educational Institutions, Artificial Intelligence, Systems
Summary:
IoT-based gadgets are increasingly being adopted for use at every cadre of the educational institutions ranging from pre-school to high school and more importantly at higher institutions of learning among different groups of learners and educators. These gadgets collectively referred to as the “Internet -of- Educational Things (IoET)” digitize the ways data are collected, kept and used for educational purposes.
Despite the added advantages and benefits derived from the continuous usage of the numerous IoET gadgets among institutional administrators, students and educators, the choice of determining the best set of IoET for the varied categories of learners poses a great challenge to both educators and administrators. This workshop calls for works and current research utilizing various Artificial Intelligent (A.I) systems, and innovative machine learning models to unravel and resolve the ambiguities in the choice, classification and selection of the best Internet-of-Educational -Things for use in both physical and online (virtual) locations among different categories of learners. Its topics of interest include, but are not limited to the following:
·Embedded algorithms for the selection of Educational IoTs.
·Pattern matching and behavioral analysis of students with Educational IoT-based gadgets.
·Machine learning models for classification of learners’ achievement in IoT classrooms.
·Developed and deployed educational IoT prototypes among different categories of learners.
·Deep learning models in Educational-based IoT gadget selections.
Chair: Femi Temitope Johnson
| Federal University of Agriculture, Abeokuta, Nigeria
Femi Temitope Johnson is a researcher at the College of Physical Sciences, Department of Computer science, Federal University of Agriculture, (FUNAAB), Abeokuta, Nigeria. As an astute researcher with several years of experience in the field of Artificial Intelligence, Machine learning, and Robotics, he has channeled his research to various domains including education, agriculture, security, medicine and healthcare, and recently to human resources development for both national and international benefits.
Over the years, he has developed Artificial Intelligent (A.I) enabled systems and highly accurate machine learning models, presented research findings at both local and international conferences with scholarly papers published in conference proceedings, high impact journals including Springer, IEEE, SCOPUS, and other international accredited university journals around the globe. He currently acts as editor and reviewer for some international journals including the International Journal of Research and Innovation in Applied Science (IJRIAS), Iraqi Journal of Science (A Multi-disciplinary Journal PublishedbyBaghdad University, Iraq), Academia Letters, etc. Besides, Femi Johnson heads the Computer Science Research and Edu-Tech Group of the Bethel American International School, Nigeria.
He is also a member of professional bodies including Computer Professionals of Nigeria (CPN), Internet Society of Nigeria (ISOC), Data Science Nigeria (DSN), The Asia Society of Researchers (ASR), The International Management Research and Technology Consortium (IMRTC), USA, The Society of Digital Information and Wireless Communications (SDIWC), International Society for Research and Development (ISRD), the International Association of Computer Scientists and Information Technologists (IACSIT) and an associate member of the European EURO Science Research Group, France.