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Todd Whitney; Justin T. Cooper; Kate Snider – Journal of Special Education Technology, 2025
Students with disabilities are being educated in the general education setting at an ever-increasing rate; therefore, it becomes imperative that educator preparation programs ensure that pre-service teachers are equipped to meet the needs of all students. Teacher education scholars have encouraged educator preparation programs to maximize the…
Descriptors: Students with Disabilities, Preservice Teachers, Preservice Teacher Education, Inclusion
Piro, Jody S. – International Journal of Educational Leadership Preparation, 2023
Debriefing may be the most important factor for learning in simulations. This exploratory case study investigated a modified Plus-Delta approach to debriefings following mixed reality simulation-based learning. The findings suggested that educational leadership students who encountered debriefings from simulations developed leadership skills and…
Descriptors: Computer Simulation, Administrator Education, Leadership Qualities, Skill Development
Rhonda Bondie; Elizabeth City – Learning Professional, 2024
New questions and concerns arise every day about the impact of AI in schools, such as how teachers will learn about AI and leverage it in their classrooms, how they can use it to develop their own teaching expertise, and if AI for educators really leads to better teaching and learning. The authors believe that AI can help teachers become more…
Descriptors: Preservice Teacher Education, Artificial Intelligence, Computer Simulation, Microteaching
Evaluative Feedback Isn't Enough: Harnessing the Power of Consequential Feedback in Higher Education
Kathleen M. Quinlan; Edd Pitt – Assessment & Evaluation in Higher Education, 2025
Most research on feedback in higher education focuses on evaluative feedback and its recipience, uptake, and enactment. Evaluative feedback information includes judgments, critiques and suggestions for improvement provided by a teacher, peer, self, pre-programmed automatic feedback, or artificial intelligence tutoring systems. In contrast, we…
Descriptors: Feedback (Response), Concept Formation, Higher Education, Health Education
Gundel, Erik; Piro, Jody S. – Action in Teacher Education, 2021
The purpose of this multiple case study was to gain insight into the self-efficacy beliefs of pre-service teachers participating in a teacher education curriculum that used mixed reality simulation experiences. Qualitative data were collected from a purposeful sampling of 49 student participants (n = 49), as well as 5 professional participants (n…
Descriptors: Student Attitudes, Self Efficacy, Computer Simulation, Preservice Teachers
Unyaparn Sinlapaninman; Wannatida Yonwilad – Educational Process: International Journal, 2025
Background/purpose: This study addresses the growing gap in preparing pre-service teachers for immersive digital instruction, especially within metaverse environments. While many are familiar with digital platforms as learners, they often lack practical experience in managing and teaching in virtual settings. The purpose of this study is to…
Descriptors: Preservice Teachers, Electronic Learning, Computer Simulation, Instructional Design
Hayden P. Smith; Bobbie Ticknor; Alicia H. Sitren – Journal of Educators Online, 2024
The role of emotion in the context of virtual reality learning environments (VRLEs) has lately received increased attention, though there is a gap in the research on VRLEs in criminal justice. The current study examines the impact of a virtual reality experience that focuses on mental illness occurring in those within the criminal justice system.…
Descriptors: Computer Simulation, Criminal Law, Justice, Law Enforcement
Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
Sanghoon Park; Heoncheol Yun – Educational Technology Research and Development, 2024
Although numerous studies have demonstrated the potential benefits of augmented reality (AR) in education, the influence of education students' learning experiences on their AR technology acceptance in the classroom has yet to be examined thoroughly. In this empirical study, we explored the affective experiences (i.e., positive emotions, negative…
Descriptors: Computer Simulation, Educational Technology, Student Experience, Emotional Response
Eric Jordan Mendelson – ProQuest LLC, 2022
Educators and researchers have long contemplated the most effective ways to provide feedback to students, to build sustainable feedback practices, and to establish feedback literacy. While a considerable amount of research, theory, and practical approaches exist to support the effect of formative feedback practices, less research exists on the…
Descriptors: Preservice Teachers, Teacher Attitudes, Feedback (Response), Data Use
Chloe Hurrell; Andreas Chai; Heather Green; Graham Bradley – Environmental Education Research, 2024
The pressing urgency of climate change calls for advancements in the way it is communicated. One communication option is the use of virtual reality (VR). The current research comprised two pilot studies to develop and test a virtual environment depicting proximal effects of climate change regarding the participant's location. In both studies, an…
Descriptors: Computer Simulation, Climate, Conservation (Environment), Intention
Shahper Richter; Shohil Kishore; Inna Piven; Patrick Dodd; Guy Bate – British Journal of Educational Technology, 2025
This study investigates how anthropomorphic AI chatbot avatars, designed in line with the Stereotype Content Model (SCM) dimensions of warmth and competence, influence university students' perceptions of support for self-directed learning (SDL) activities. We examined student responses to two distinct avatars--one projecting warmth and the other…
Descriptors: Artificial Intelligence, Technology Uses in Education, Postsecondary Education, Computer Simulation
Katerina Nerantzaki; Polykarpos Meladianos – Journal of Interactive Learning Research, 2025
Virtual reality (VR) technology has greatly expanded its role in education. It provides immersive experiences that enhance learning across various disciplines. Understanding the role of emotions in technology-based learning environments is crucial for improving educational outcomes. This study aimed to explore how VR environments evoke university…
Descriptors: Computer Simulation, Technology Uses in Education, Emotional Response, Undergraduate Students
Adam Carreon; Caitlin Criss; Samantha Goldman – Journal of Interactive Learning Research, 2024
This alternating treatment design single case study investigated the effects of email-based performance feedback (PF) and immersive video within virtual reality (IVVR) on two preservice teachers' use of behavior-specific praise (BSP). Both participants demonstrated increased BSP rates following both intervention conditions. Findings suggest that…
Descriptors: Preservice Teachers, Coaching (Performance), Positive Reinforcement, Computer Simulation
Kristyn Wilson; Anna M. Yonas – Teachers College Record, 2024
Background: Mixed reality simulations (MRSs) offer a platform for novice teachers to practice teaching in the absence of real children. Though prior research suggests simulated practice can be beneficial for novice teachers, these studies describe widely varying designs and implementations across teacher education programs (TEPs). As such, we lack…
Descriptors: Computer Simulation, Teacher Education Programs, Preservice Teacher Education, Preservice Teachers

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