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Direct linkLanders, Richard N.; Auer, Elena M.; Mersy, Gabriel; Marin, Sebastian; Blaik, Jason – International Journal of Testing, 2022
Assessment trace data, such as mouse positions and their timing, offer interesting and provocative reflections of individual differences yet are currently underutilized by testing professionals. In this article, we present a 10-step procedure to maximize the probability that a trace data modeling project will be successful: (1) grounding the…
Descriptors: Artificial Intelligence, Data Collection, Psychometrics, Data Science
Dongyu Yu; Xing Yao; Kaidi Yu; Dandan Du; Jinyi Zhi; Chunhui Jing – Interactive Learning Environments, 2024
The objective of this study was to determine the differential effects of the presentation position of the augmented reality--head worn display (AR-HWD) interface and the audiovisual-dominant multimodal learning material on learning performance and cognitive load across different learning tasks in training for high-speed train driving. We selected…
Descriptors: Artificial Intelligence, Computer Simulation, Computer Peripherals, Computer Interfaces
Azzah Al-Maskari; Thuraya Al Riyami; Sami Ghnimi – Journal of Applied Research in Higher Education, 2024
Purpose: Knowing the students' readiness for the fourth industrial revolution (4IR) is essential to producing competent, knowledgeable and skilled graduates who can contribute to the skilled workforce in the country. This will assist the Higher Education Institutions (HEIs) to ensure that their graduates own skill sets needed to work in the 4IR…
Descriptors: Career Readiness, Technological Literacy, Student Attitudes, Information Technology
Download full textVásquez-Carbonell, Mauricio – Digital Education Review, 2022
Augmented Reality (AR) is a technology that has benefited from the massification of computational devices, putting it in the focus of researchers as a novel teaching aid in engineering. For this very reason, a great amount of information about AR in engineering education is emerging constantly. To synthesize the information, a Systematic…
Descriptors: Computer Software, Computer Simulation, Artificial Intelligence, Engineering Education
Junhong Xiao; Aras Bozkurt; Mark Nichols; Angelica Pazurek; Christian M. Stracke; John Y. H. Bai; Robert Farrow; Dónal Mulligan; Chrissi Nerantzi; Ramesh Chander Sharma; Lenandlar Singh; Isak Frumin; Andrew Swindell; Sarah Honeychurch; Melissa Bond; Jon Dron; Stephanie Moore; Jing Leng; Patricia J. Slagter van Tryon; Manuel Garcia; Evgeniy Terentev; Ahmed Tlili; Thomas K. F. Chiu; Charles B. Hodges; Petar Jandric; Alexander Sidorkin; Helen Crompton; Stefan Hrastinski; Apostolos Koutropoulos; Mutlu Cukurova; Peter Shea; Steven Watson; Kai Zhang; Kyungmee Lee; Eamon Costello; Mike Sharples; Anton Vorochkov; Bryan Alexander; Maha Bali; Robert L. Moore; Olaf Zawacki-Richter; Tutaleni Iita Asino; Henk Huijser; Chanjin Zheng; Sunagül Sani-Bozkurt; Josep M. Duart; Chryssa Themeli – TechTrends: Linking Research and Practice to Improve Learning, 2025
Advocates of AI in Education (AIEd) assert that the current generation of technologies, collectively dubbed artificial intelligence, including generative artificial intelligence (GenAI), promise results that can transform our conceptions of what education looks like. Therefore, it is imperative to investigate how educators perceive GenAI and its…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Trends, Trend Analysis
Peer reviewedParmentier, Christophe – Education and Computing, 1988
Discusses computer science developments in French primary schools and describes strategies for using computers in the classroom most efficiently. Highlights include the use of computer networks; software; artificial intelligence and expert systems; computer-assisted learning (CAL) and intelligent CAL; computer peripherals; simulation; and teaching…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Computer Literacy, Computer Networks
