Publication Date
In 2025 | 4 |
Since 2024 | 123 |
Since 2021 (last 5 years) | 316 |
Since 2016 (last 10 years) | 385 |
Since 2006 (last 20 years) | 448 |
Descriptor
Source
Author
Hwang, Gwo-Jen | 5 |
Adams Becker, S. | 4 |
Xing, Wanli | 4 |
Burgos, Daniel, Ed. | 3 |
Cummins, M. | 3 |
Estrada, V. | 3 |
Isaias, Pedro, Ed. | 3 |
Johnson, L. | 3 |
Li, Chenglu | 3 |
McCormack, Mark | 3 |
Seepersaud, Deborah, Ed. | 3 |
More ▼ |
Publication Type
Education Level
Location
China | 22 |
Taiwan | 18 |
Australia | 14 |
Canada | 11 |
Germany | 9 |
Spain | 9 |
Turkey | 9 |
Hong Kong | 8 |
Thailand | 8 |
United States | 8 |
Saudi Arabia | 7 |
More ▼ |
Laws, Policies, & Programs
Assessments and Surveys
Massachusetts Comprehensive… | 1 |
Motivated Strategies for… | 1 |
National Assessment Program… | 1 |
What Works Clearinghouse Rating
Zhengze Li; Hui Chen; Xin Gao – Education and Information Technologies, 2024
Online supplementary education has been prevalent in recent years due to the advent of technology (e.g., live streaming) and the COVID-19 pandemic. However, the performance of students in this mode of education varies greatly, and the underlying reasons are yet to be investigated. This study aims to understand the impact of various factors and…
Descriptors: Predictor Variables, Elementary School Students, Electronic Learning, Supplementary Education
Mia Allen; Usman Naeem; Sukhpal Singh Gill – IEEE Transactions on Education, 2024
Contributions: In this article, a generative artificial intelligence (AI)-based Q&A system has been developed by integrating information retrieval and natural language processing techniques, using course materials as a knowledge base and facilitating real-time student interaction through a chat interface. Background: The rise of advanced AI…
Descriptors: Artificial Intelligence, Technology Uses in Education, Information Retrieval, Natural Language Processing
Desheng Yan; Guangming Li – Interactive Learning Environments, 2024
Smart education, with its intelligent, individualized, and technologized content, represents people's lofty expectations for future education. It provides a good learning platform for teaching and an important environment in which students' digital learning power can be developed in the context of the information technology era. Digital learning…
Descriptors: Electronic Learning, Information Technology, Artificial Intelligence, Educational Environment
Kong, Siu-Cheung; Cheung, William Man-Yin; Tsang, Olson – Education and Information Technologies, 2023
Artificial intelligence (AI) literacy education for senior secondary students can prepare them for an AI-pervasive future. Although senior secondary students have been targeted, whether they can learn abstract AI concepts, feel empowered to harness AI and understand AI ethical issues is under-researched. We report a 34-h AI literacy programme with…
Descriptors: Program Evaluation, Artificial Intelligence, Literacy Education, Secondary School Students
Kuadey, Noble Arden; Mahama, Francois; Ankora, Carlos; Bensah, Lily; Maale, Gerald Tietaa; Agbesi, Victor Kwaku; Kuadey, Anthony Mawuena; Adjei, Laurene – Interactive Technology and Smart Education, 2023
Purpose: This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms. Design/methodology/approach: The proposed model for this study adopted a unified theory of acceptance and use of technology…
Descriptors: Foreign Countries, College Students, Learning Management Systems, Student Behavior
Liu, Chunhong; Zhang, Haoyang; Zhang, Jieyu; Zhang, Zhengling; Yuan, Peiyan – International Journal of Information and Communication Technology Education, 2023
Current learning platforms generally have problems such as fragmented knowledge, redundant information, and chaotic learning routes, which cannot meet learners' autonomous learning requirements. This paper designs a learning path recommendation system based on knowledge graphs by using the characteristics of knowledge graphs to structurally…
Descriptors: Educational Technology, Artificial Intelligence, Electronic Learning, Concept Mapping
Knapp, David H.; Powell, Bryan; Smith, Gareth D.; Coggiola, John C.; Kelsey, Matthew – Research Studies in Music Education, 2023
The COVID-19 pandemic prompted a sudden rethinking of how music was taught and learned. Prior to the pandemic, the web-based digital audio workstation Soundtrap emerged as a leading platform for creating music online. The present study examined the growth of Soundtrap's usage during the COVID-19 pandemic. Using machine-learning methods, we…
Descriptors: Technology Uses in Education, Audio Equipment, COVID-19, Pandemics
Bauer, Elisabeth; Greisel, Martin; Kuznetsov, Ilia; Berndt, Markus; Kollar, Ingo; Dresel, Markus; Fischer, Martin R.; Fischer, Frank – British Journal of Educational Technology, 2023
Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a…
Descriptors: Peer Relationship, Feedback (Response), Artificial Intelligence, Natural Language Processing
Predicting Primary and Middle-School Students' Preferences for Online Learning with Machine Learning
V. Selvakumar; Tilak Pakki Venkata; Teja Pakki Venkata; Shubham Singh – South African Journal of Childhood Education, 2023
Background: The COVID-19 pandemic has brought attention to student psychological wellness. Because of isolation, lack of socialisation and intellectual and physical development from excessive media use, primary and secondary school students are at high risk for health problems. Aim: This study aimed to identify the most effective machine learning…
Descriptors: Elementary School Students, Middle School Students, Preferences, Online Courses
Rosmansyah, Yusep; Putro, Budi Laksono; Putri, Atina; Utomo, Nur Budi; Suhardi – Interactive Learning Environments, 2023
In this article, smart learning environment (SLE) is defined as a hybrid learning system that provides learners and other stakeholders with a joyful learning process while achieving learning outcomes as a result of the employed intelligent tools and techniques. From literature study, existing SLE models and frameworks are difficult to understand…
Descriptors: Electronic Learning, Artificial Intelligence, Educational Technology, Technology Uses in Education
Elbawab, Mohamed; Henriques, Roberto – Education and Information Technologies, 2023
Electronic learning (e-learning) is considered the new norm of learning. One of the significant drawbacks of e-learning in comparison to the traditional classroom is that teachers cannot monitor the students' attentiveness. Previous literature used physical facial features or emotional states in detecting attentiveness. Other studies proposed…
Descriptors: Students, Electronic Learning, Attention Span, Artificial Intelligence
Ouyang, Fan; Zheng, Luyi; Jiao, Pengcheng – Education and Information Technologies, 2022
As online learning has been widely adopted in higher education in recent years, artificial intelligence (AI) has brought new ways for improving instruction and learning in online higher education. However, there is a lack of literature reviews that focuses on the functions, effects, and implications of applying AI in the online higher education…
Descriptors: Artificial Intelligence, Electronic Learning, Higher Education, Literature Reviews
Robert F. Siegle; Scotty D. Craig – Journal of Computer Assisted Learning, 2024
Background: The voices virtual on-screen characters use has been shown to impact learning and perception outcomes. Recent replication research on these voices showed that synthetic voices were not a detriment if produced by a high-quality engine with clear articulation. The current manuscript examines previous accent research that utilized now…
Descriptors: Acoustics, Artificial Intelligence, Electronic Learning, Quality Assurance
Lincke, Alisa; Jansen, Marc; Milrad, Marcelo; Berge, Elias – Research and Practice in Technology Enhanced Learning, 2021
Web-based learning systems with adaptive capabilities to personalize content are becoming nowadays a trend in order to offer interactive learning materials to cope with a wide diversity of students attending online education. Learners' interaction and study practice (quizzing, reading, exams) can be analyzed in order to get some insights into the…
Descriptors: Artificial Intelligence, Prediction, Electronic Learning, Repetition
Lwande, Charles; Oboko, Robert; Muchemi, Lawrence – Education and Information Technologies, 2021
Learning Management Systems (LMS) lack automated intelligent components that analyze data and classify learners in terms of their respective characteristics. Manual methods involving administering questionnaires related to a specific learning style model and cognitive psychometric tests have been used to identify such behavior. The problem with…
Descriptors: Integrated Learning Systems, Student Behavior, Prediction, Artificial Intelligence