Publication Date
| In 2026 | 0 |
| Since 2025 | 2438 |
| Since 2022 (last 5 years) | 5993 |
| Since 2017 (last 10 years) | 6937 |
| Since 2007 (last 20 years) | 7394 |
Descriptor
Source
Author
| Danielle S. McNamara | 26 |
| Gwo-Jen Hwang | 19 |
| Mihai Dascalu | 17 |
| McNamara, Danielle S. | 14 |
| Hwang, Gwo-Jen | 13 |
| Aleven, Vincent | 12 |
| Jiahong Su | 12 |
| Wanli Xing | 12 |
| Chenglu Li | 11 |
| Dragan Gaševic | 11 |
| Koedinger, Kenneth R. | 11 |
| More ▼ | |
Publication Type
Education Level
Audience
| Researchers | 244 |
| Teachers | 208 |
| Practitioners | 181 |
| Policymakers | 88 |
| Administrators | 72 |
| Students | 55 |
| Media Staff | 10 |
| Counselors | 5 |
| Parents | 4 |
| Support Staff | 4 |
| Community | 3 |
| More ▼ | |
Location
| China | 391 |
| Turkey | 199 |
| Australia | 131 |
| United States | 123 |
| Taiwan | 118 |
| United Kingdom | 116 |
| India | 107 |
| South Korea | 99 |
| Canada | 92 |
| Germany | 92 |
| Indonesia | 92 |
| More ▼ | |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
| Meets WWC Standards without Reservations | 1 |
| Meets WWC Standards with or without Reservations | 1 |
Nye, Benjamin D. – International Journal of Artificial Intelligence in Education, 2016
Advanced learning technologies are reaching a new phase of their evolution where they are finally entering mainstream educational contexts, with persistent user bases. However, as AIED scales, it will need to follow recent trends in service-oriented and ubiquitous computing: breaking AIED platforms into distinct services that can be composed for…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Integrated Learning Systems
Dorça, Fabiano A.; Araújo, Rafael D.; de Carvalho, Vitor C.; Resende, Daniel T.; Cattelan, Renan G. – Informatics in Education, 2016
Content personalization in educational systems is an increasing research area. Studies show that students tend to have better performances when the content is customized according to his/her preferences. One important aspect of students particularities is how they prefer to learn. In this context, students learning styles should be considered, due…
Descriptors: Resource Units, Cognitive Style, Automation, Individualized Instruction
Twenty Years On: Reflections on "Supporting the Use of External Representations in Problem Solving"…
Cox, Richard; Brna, Paul – International Journal of Artificial Intelligence in Education, 2016
We reflect upon a paper we wrote that was published in 1995 (20 years ago). We outline the motivation for the work and situate it in the state of the art at that time. We suggest that a key contribution was to highlight the need to provide support for learners who reason with external representations. The support must be flexible enough to…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Problem Solving, Cognitive Processes
Chen, Jing; Fife, James H.; Bejar, Isaac I.; Rupp, André A. – ETS Research Report Series, 2016
The "e-rater"® automated scoring engine used at Educational Testing Service (ETS) scores the writing quality of essays. In the current practice, e-rater scores are generated via a multiple linear regression (MLR) model as a linear combination of various features evaluated for each essay and human scores as the outcome variable. This…
Descriptors: Scoring, Models, Artificial Intelligence, Automation
Sharma, Arjun; Biswas, Arijit; Gandhi, Ankit; Patil, Sonal; Deshmukh, Om – International Educational Data Mining Society, 2016
Online educational videos have emerged as one of the most popular modes of learning in the recent years. Studies have shown that liveliness is highly correlated to engagement in educational videos. While previous work has focused on feature engineering to estimate liveliness and that too using only the acoustic information, in this paper we…
Descriptors: Video Technology, Audiovisual Aids, Artificial Intelligence, Prediction
Chrysafiadi, Konstantina; Troussas, Christos; Virvou, Maria – International Journal of Learning Technology, 2022
This paper addresses the interesting issue of mobile-assisted language learning using novel techniques for further improving the adaptivity and personalisation to students. The domain model of the system includes English and French language concepts, and its user model holds information about students and their progress. It also embodies a…
Descriptors: Second Language Learning, Second Language Instruction, English (Second Language), French
Hsu, Liwei – Computer Assisted Language Learning, 2022
The English as a foreign language (EFL) learners' levels of attention and meditation as well as brainwaves while interacting with an interlocutor in three different second-language (L2) socialization contexts--with another human in person, with another person through a virtual platform, and with an artificial intelligence (AI) chatbot--were…
Descriptors: Computer Assisted Instruction, Teaching Methods, English (Second Language), Second Language Learning
Goger, Annelies; Parco, Allyson; Vegas, Emiliana – Brookings Institution, 2022
The rapid expansion of new technologies into every sector has contributed to the proliferation of alternative models of education, learning, and skill signaling in global labor markets. From digital badges to bootcamps to learning and employment records (LERs), a wide range of public, private, and nonprofit initiatives and platforms have emerged…
Descriptors: Technological Literacy, Information Technology, Credentials, Information Storage
Pelletier, Kathe; McCormack, Mark; Reeves, Jamie; Robert, Jenay; Arbino, Nichole – EDUCAUSE, 2022
Two years into the COVID-19 pandemic, much still feels the same, though in some important ways thinking and behaviors may be shifting in anticipation of longer-term changes in the ways lives are structured and how places and spaces are shared. In higher education, these shifts may reflect an evolution from short-term "emergency" or…
Descriptors: Higher Education, Educational Trends, COVID-19, Pandemics
Holmes, Mike; Latham, Annabel; Crockett, Keeley; O'Shea, James D. – IEEE Transactions on Learning Technologies, 2018
Comprehension is an important cognitive state for learning. Human tutors recognize comprehension and non-comprehension states by interpreting learner non-verbal behavior (NVB). Experienced tutors adapt pedagogy, materials, and instruction to provide additional learning scaffold in the context of perceived learner comprehension. Near real-time…
Descriptors: Comprehension, Classification, Artificial Intelligence, Networks
Moreno-Estevaa, Enrique Garcia; White, Sonia L. J.; Wood, Joanne M.; Black, Alex A. – Frontline Learning Research, 2018
In this research, we aimed to investigate the visual-cognitive behaviours of a sample of 106 children in Year 3 (8.8 ± 0.3 years) while completing a mathematics bar-graph task. Eye movements were recorded while children completed the task and the patterns of eye movements were explored using machine learning approaches. Two different techniques of…
Descriptors: Artificial Intelligence, Man Machine Systems, Mathematics Education, Eye Movements
Edwards, Chad; Edwards, Autumn; Spence, Patric R.; Lin, Xialing – Communication Education, 2018
Human-machine communication has emerged as a new relational context of education and should become a priority for instructional scholarship in the coming years. With artificial intelligence and robots offering personalized instruction, teachers' roles may shift toward overseers who design and select machine-led instruction, monitor student…
Descriptors: Artificial Intelligence, Robotics, Teaching Methods, Teacher Role
Song, Yu; Lei, Shunwei; Hao, Tianyong; Lan, Zixin; Ding, Ying – Journal of Educational Computing Research, 2021
Due to benefits for teaching and learning, an increasing number of studies have focused on classroom dialogue and how to make it productive. Coding, in which the transcribed conversation is allocated to a set of features, is commonly employed to deal with the textual data arising from this dialogue. This is generally done manually and cannot…
Descriptors: Semantics, Classification, Classroom Communication, Dialogs (Language)
Chiu, Wang-Kin – Education Sciences, 2021
The technological advancement and rapid development of artificial intelligence have led to a growing number of studies investigating pedagogical innovations incorporated with emerging technologies in this digital era. An increasing amount of empirical evidence has suggested the potential benefits of incorporating digital technologies and…
Descriptors: Technological Advancement, Artificial Intelligence, Educational Innovation, Educational Technology
Zhang, Zhiyong; Zhang, Danyang – Grantee Submission, 2021
Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These…
Descriptors: Statistics Education, Definitions, Course Descriptions, Computer Science Education

Peer reviewed
Direct link
