NotesFAQContact Us
Collection
Advanced
Search Tips
Publication Date
In 202516
Source
Smart Learning Environments16
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Showing 1 to 15 of 16 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Reham Adel Ali; Mohamed Soliman; Muhammad Roflee Weahama; Muhammadafeefee Assalihee; Imran Mahmud – Smart Learning Environments, 2025
The current study explores metaverse adoption among higher education institutions (HEIs) in the light of a theoretical framework to empower future perspectives of the metaverse as a learning platform. Even though this technology was just recently introduced to the higher education sector, very few attempts have been made to evaluate its impact.…
Descriptors: Technology Uses in Education, College Students, Student Attitudes, Private Colleges
Peer reviewed Peer reviewed
Direct linkDirect link
Talha Mahboob Alam; George Adrian Stoica; Özlem Özgöbek – Smart Learning Environments, 2025
Response technologies (RTs), also termed clickers or student response systems, have gained traction among researchers in classrooms in recent years. RTs encompass various interactive tools and technologies that are pivotal in modern educational settings. Numerous articles emphasize the effectiveness of RTs across multiple grades and courses.…
Descriptors: Educational Technology, Technology Uses in Education, Elementary Secondary Education, Student Reaction
Peer reviewed Peer reviewed
Direct linkDirect link
Alexandre Machado; Kamilla Tenório; Mateus Monteiro Santos; Aristoteles Peixoto Barros; Luiz Rodrigues; Rafael Ferreira Mello; Ranilson Paiva; Diego Dermeval – Smart Learning Environments, 2025
Researchers are increasingly interested in enabling teachers to monitor and adapt gamification design in the context of intelligent tutoring systems (ITSs). These contributions rely on teachers' needs and preferences to adjust the gamification design according to student performance. This work extends previous studies on teachers' perception of…
Descriptors: Faculty Workload, Educational Resources, Artificial Intelligence, Technology Uses in Education
Peer reviewed Peer reviewed
Direct linkDirect link
Brian Shambare; Thuthukile Jita – Smart Learning Environments, 2025
Teaching in rural schools is frequently marked by challenges ranging from restricted technological infrastructure and geographical remoteness to sparse professional development. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), this paper reports findings from a sequential explanatory mixed-methods study that investigated…
Descriptors: Influences, Computer Simulation, Technology Uses in Education, Technology Integration
Peer reviewed Peer reviewed
Direct linkDirect link
Ka Yan Fung; Kwong Chiu Fung; Tze Leung Rick Lui; Kuen Fung Sin; Lik Hang Lee; Huamin Qu; Shenghui Song – Smart Learning Environments, 2025
In recent years, there has been a growing interest in using robots within educational environments due to their potential to augment student engagement and motivation. However, current research has not adequately addressed the effectiveness of these robots in facilitating inclusive learning for diverse student populations, particularly those with…
Descriptors: Robotics, Educational Technology, Technology Uses in Education, Interaction
Peer reviewed Peer reviewed
Direct linkDirect link
Said A. Salloum; Khaled Mohammad Alomari; Aseel M. Alfaisal; Rose A. Aljanada; Azza Basiouni – Smart Learning Environments, 2025
The integration of artificial intelligence in educational environments has the potential to revolutionize teaching and learning by enabling real-time analysis of students' emotions, which are crucial determinants of engagement, motivation, and learning outcomes. However, accurately detecting and responding to these emotions remains a significant…
Descriptors: Artificial Intelligence, Emotional Response, Psychological Patterns, Individualized Instruction
Peer reviewed Peer reviewed
Direct linkDirect link
Idit Adler; Scott Warren; Cathleen Norris; Elliot Soloway – Smart Learning Environments, 2025
Smart learning environments provide students with opportunities to engage in self-regulated learning (SRL). However, little research has examined how teachers leverage these opportunities. We employed a multiple-case study methodology to examine the SRL supporting instructional practices of five third-grade teachers as they implemented a science…
Descriptors: Elementary School Students, Elementary School Teachers, Grade 3, Independent Study
Peer reviewed Peer reviewed
Direct linkDirect link
Hanieh Shafiee Rad – Smart Learning Environments, 2025
This research delves into the transformative potential of artificial intelligence (AI) interventions in advancing reading comprehension, sparking learner engagement, and empowering self-regulated learning. It addresses a gap in the literature regarding innovative approaches to fostering these skills through emerging technologies. An AI-based…
Descriptors: Reading Comprehension, Second Language Learning, Artificial Intelligence, Learner Engagement
Peer reviewed Peer reviewed
Direct linkDirect link
Masoumeh Bagheri-Nesami; Mahsa Kamali; Amirabbas Mollaei – Smart Learning Environments, 2025
Nurses acknowledge that their understanding of the shortcomings in the utilization of medical devices stems from insufficient knowledge about their correct usage. The use of Quick Response (QR) technology has paved a new gateway for accessing information and resources. The available evidence confirmed the usefulness of QR codes for use in clinical…
Descriptors: Medical Services, Handheld Devices, Knowledge Level, Clinical Experience
Peer reviewed Peer reviewed
Direct linkDirect link
Nurassyl Kerimbayev; Karlygash Adamova; Rustam Shadiev; Zehra Altinay – Smart Learning Environments, 2025
This review was conducted in order to determine the specific role of intelligent technologies in the individual learning experience. The research work included consider articles published between 2014 and 2024, found in Web of Science, Scopus, and ERIC databases, and selected among 933 ?articles on the topic. Materials were checked for compliance…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Computer Software, Databases
Peer reviewed Peer reviewed
Direct linkDirect link
Yan Li; Xinyan Zhou; Hong-biao Yin; Thomas K. F. Chiu – Smart Learning Environments, 2025
Artificial Intelligence (AI) chatbots, with their ability to engage in conversations that resemble human interactions, have been increasingly applied to language teaching. Most recent review studies overlook student learning outcomes and the methods to achieve these outcomes in chatbot-supported language learning. Activity Theory (AT) offers a…
Descriptors: Artificial Intelligence, Educational Technology, Language Acquisition, Outcomes of Education
Peer reviewed Peer reviewed
Direct linkDirect link
Amir Reza Rahimi; Zahra Mosalli – Smart Learning Environments, 2025
Researchers have significantly explored language learners' attitudes toward ChatGPT through the lens of technology acceptance models, particularly with its development and integration into computer-assisted language learning (CALL). However, further research in this area is necessary to apply a theoretical framework with a pedagogical-oriented…
Descriptors: Second Language Learning, Second Language Instruction, Artificial Intelligence, Technology Uses in Education
Peer reviewed Peer reviewed
Direct linkDirect link
Segun Michael Ojetunde; Umesh Ramnarain – Smart Learning Environments, 2025
Learning interaction patterns is key to the explanation of learning outcomes. Different studies have reported the relationship between classroom process variables and learning outcomes in a traditional classroom setting. However, the advent of robotics and its attendant student-robot interaction moderated by students' mathematical ability is yet…
Descriptors: Robotics, Technology Uses in Education, Mathematics Skills, Outcomes of Education
Peer reviewed Peer reviewed
Direct linkDirect link
Jennifer A. Cardenas Castaneda; Pei-Chun Lin; Patrick C. K. Hung; Hua-Xu Zhong; Hao-An Tseng; Yung-Fa Huang; Rafiq Ahmad – Smart Learning Environments, 2025
Education in science, technology, engineering, and mathematics (STEM) is essential to achieving continued technological advancement. The most critical years for instilling knowledge are during childhood, and a strategic way to accomplish this is through playful materials. Therefore, there is a need to develop more inclusive solutions to achieve…
Descriptors: Instructional Design, STEM Education, Visual Impairments, Students with Disabilities
Peer reviewed Peer reviewed
Direct linkDirect link
David Antonio Rosas; Natalia Padilla-Zea; Daniel Burgos – Smart Learning Environments, 2025
This paper advances in the understanding of motivation in terms of flow in groups from a physiological perspective. We use wearable devices to monitor the heart rate variation during a set of sessions of face-to-face STEAM project-based learning. By using Action Research with mixed-methods design, we observed a set of 28 students in real-world…
Descriptors: Modeling (Psychology), Physiology, STEM Education, Art Education
Previous Page | Next Page »
Pages: 1  |  2