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Direct linkToyokawa, Yuko; Horikoshi, Izumi; Majumdar, Rwitajit; Ogata, Hiroaki – Smart Learning Environments, 2023
In inclusive education, students with different needs learn in the same context. With the advancement of artificial intelligence (AI) technologies, it is expected that they will contribute further to an inclusive learning environment that meets the individual needs of diverse learners. However, in Japan, we did not find any studies exploring…
Descriptors: Barriers, Affordances, Artificial Intelligence, Inclusion
Lin, Chien-Chang; Huang, Anna Y. Q.; Lu, Owen H. T. – Smart Learning Environments, 2023
Sustainable education is a crucial aspect of creating a sustainable future, yet it faces several key challenges, including inadequate infrastructure, limited resources, and a lack of awareness and engagement. Artificial intelligence (AI) has the potential to address these challenges and enhance sustainable education by improving access to quality…
Descriptors: Artificial Intelligence, Educational Technology, Sustainability, Technology Uses in Education
Kohei Nakamura; Manabu Ishihara; Izumi Horikoshi; Hiroaki Ogata – Smart Learning Environments, 2024
Expectations of big data across various fields, including education, are increasing. However, uncovering valuable insights from big data is like locating a needle in a haystack, and it is difficult for teachers to use educational big data on their own. This study aimed to understand changes in student participation rates during classes and…
Descriptors: Foreign Countries, Junior High School Students, Junior High School Teachers, Public Schools
Benita, Francisco; Virupaksha, Darshan; Wilhelm, Erik; Tunçer, Bige – Smart Learning Environments, 2021
This paper proposes an Internet of Things device (IoT)-based ecosystem that can be leveraged to provide children and adolescent students with STEM educational activities. Our framework is general and scalable, covering multi-stakeholder partnerships, learning outcomes, educational program design and technical architecture. We highlight the…
Descriptors: Data Use, STEM Education, Technology Integration, Internet
Papamitsiou, Zacharoula; Filippakis, Michail E.; Poulou, Marilena; Sampson, Demetrios; Ifenthaler, Dirk; Giannakos, Michail – Smart Learning Environments, 2021
In the era of digitalization of learning and teaching processes, Educational Data Literacy (EDL) is highly valued and is becoming essential. EDL is conceptualized as the ability to collect, manage, analyse, comprehend, interpret, and act upon educational data in an ethical, meaningful, and critical manner. The professionals in the field of…
Descriptors: Multiple Literacies, Instructional Design, Tutors, Electronic Learning
Alturki, Sarah; Cohausz, Lea; Stuckenschmidt, Heiner – Smart Learning Environments, 2022
The tremendous growth in electronic educational data creates the need to have meaningful information extracted from it. Educational Data Mining (EDM) is an exciting research area that can reveal valuable knowledge from educational databases. This knowledge can be used for many purposes, including identifying dropouts or weak students who need…
Descriptors: Information Retrieval, Data Analysis, Data Use, Prediction
