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R. K. Kapila Vani; P. Jayashree – Education and Information Technologies, 2025
Emotions of learners are fundamental and significant in e-learning as they encourage learning. Machine learning models are presented in the literature to look at how emotions may affect e-learning results that are improved and optimized. Nevertheless, the models that have been suggested so far are appropriate for offline mode, whereby data for…
Descriptors: Electronic Learning, Psychological Patterns, Artificial Intelligence, Models
Kok-Sing Tang – Science Education, 2024
Research in languages and literacies in science education (LLSE) has developed substantial theoretical and pedagogical insights into how students learn science through language, discourse, and multimodal representations. At the same time, language is central to the functioning of generative artificial intelligence (GenAI). On this common basis…
Descriptors: Artificial Intelligence, Meta Analysis, Science Education, Language Usage
Mengjiao Yin; Hengshan Cao; Zuhong Yu; Xianyu Pan – International Journal of Web-Based Learning and Teaching Technologies, 2024
This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a…
Descriptors: Predictor Variables, Artificial Intelligence, Performance, Cluster Grouping
Shilpi Harnal; Gaurav Sharma; Anupriya; Anand Muni Mishra; Deepak Bagga; Nikhil Saini; Pankaj Kumar Goley; Kumar Anupam – Journal of Computer Assisted Learning, 2024
Background: An innovative and interactive real-world environment can be presented with augmented reality (AR) that comprises digital visual elements, audio, or other sensory information delivered via technology to enhance one's experience. AR has numerous potential applications in various everyday fields. The education sector is one such arena…
Descriptors: Bibliometrics, Computer Simulation, Artificial Intelligence, Educational Technology
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
Weipeng Yang; Xinyun Hu; Ibrahim H. Yeter; Jiahong Su; Yuqin Yang; John Chi-Kin Lee – Journal of Computer Assisted Learning, 2024
Background: Artificial Intelligence (AI) literacy is a crucial part of digital literacy that all individuals should possess in today's technologically advanced world. Despite the potential benefits that AI education offers, little research has been done on how to teach AI literacy to children. Objectives: This study aimed to fill that gap by…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Digital Literacy
Ai-Chu Elisha Ding – Journal of Research on Technology in Education, 2024
Multilingual learners (MLs) often struggle with science conceptual learning partly due to the abstractness of the concepts and the complexity of scientific texts. This study presents a case of a Virtual Reality (VR) enhanced science learning unit to support middle-school students' science conceptual learning. Using a transformative mixed methods…
Descriptors: Multilingualism, Science Education, Learning Processes, Computer Simulation
Sage Love; Melissa Blankstein – ITHAKA S+R, 2024
The 2024 US Instructor Survey examines the instructional needs and practices of faculty at four-year colleges and universities across the United States and sheds light on how college instructors are adapting from the COVID-19 pandemic, with a renewed focus on diverse teaching and learning modalities. This new iteration of the survey is designed to…
Descriptors: National Surveys, College Faculty, Higher Education, Adjustment (to Environment)
Pelletier, Kathe; Robert, Jenay; Muscanell, Nicole; McCormack, Mark; Reeves, Jamie; Arbino, Nichole; Grajek, Susan – EDUCAUSE, 2023
Artificial intelligence (AI) has taken the world by storm, with new AI-powered tools such as ChatGPT opening up new opportunities in higher education for content creation, communication, and learning, while also raising new concerns about the misuses and overreach of technology. Our shared humanity has also become a key focal point within higher…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Trends, Higher Education
Li, Hang; Ding, Wenbiao; Liu, Zitao – International Educational Data Mining Society, 2020
With the rapid emergence of K-12 online learning platforms, a new era of education has been opened up. It is crucial to have a dropout warning framework to preemptively identify K-12 students who are at risk of dropping out of the online courses. Prior researchers have focused on predicting dropout in Massive Open Online Courses (MOOCs), which…
Descriptors: At Risk Students, Online Courses, Elementary Secondary Education, Learning Modalities
Gupta, Sambhav; Chen, Yu – Journal of Information Systems Education, 2022
Supporting student academic success has been one of the major goals for higher education. However, low teacher-to-student ratio makes it difficult for students to receive sufficient and personalized support that they might want to. The advancement of artificial intelligence (AI) and conversational agents, such as chatbots, has provided…
Descriptors: Inclusion, Undergraduate Students, At Risk Students, Artificial Intelligence