Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 3 |
| Since 2007 (last 20 years) | 4 |
Descriptor
| Academic Achievement | 4 |
| Identification | 4 |
| College Students | 3 |
| At Risk Students | 2 |
| Classification | 2 |
| Electronic Learning | 2 |
| Feedback (Response) | 2 |
| Foreign Countries | 2 |
| Academic Failure | 1 |
| Art Appreciation | 1 |
| Art Education | 1 |
| More ▼ | |
Source
| Interactive Learning… | 4 |
Author
| Fernández-Castro, Isabel | 1 |
| Fong-Ming Shyu | 1 |
| Gwo-Jen Hwang | 1 |
| Huang, Anna Y. Q. | 1 |
| Huang, Jeff C. H. | 1 |
| Huang, Yueh-Min | 1 |
| Hwang, Wu-Yuin | 1 |
| Lu, Owen H. T. | 1 |
| Lu-Ho Hsia | 1 |
| Min-Chi Chiu | 1 |
| Rodríguez, Clemente | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 4 |
| Reports - Research | 3 |
| Reports - Evaluative | 1 |
Education Level
| Higher Education | 3 |
| Postsecondary Education | 3 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Min-Chi Chiu; Gwo-Jen Hwang; Lu-Ho Hsia; Fong-Ming Shyu – Interactive Learning Environments, 2024
In a conventional art course, it is important for a teacher to provide feedback and guidance to individual students based on their learning status. However, it is challenging for teachers to provide immediate feedback to students without any aid. The advancement of artificial intelligence (AI) has provided a possible solution to cope with this…
Descriptors: Art Education, Artificial Intelligence, Teaching Methods, Comparative Analysis
Huang, Anna Y. Q.; Lu, Owen H. T.; Huang, Jeff C. H.; Yin, C. J.; Yang, Stephen J. H. – Interactive Learning Environments, 2020
In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…
Descriptors: Academic Achievement, Data Use, Learning Analytics, Classification
Ruiz, Samara; Urretavizcaya, Maite; Rodríguez, Clemente; Fernández-Castro, Isabel – Interactive Learning Environments, 2020
A positive emotional state of students has proved to be essential for favouring student learning, so this paper explores the possibility of obtaining student feedback about the emotions they feel in class in order to discover emotion patterns that anticipate learning failures. From previous studies about emotions relating to learning processes, we…
Descriptors: College Students, Computer Science Education, Emotional Response, Student Reaction
Hwang, Wu-Yuin; Huang, Yueh-Min; Wu, Sheng-Yi – Interactive Learning Environments, 2011
The use of instant messaging to support e-learning will continue to gain importance because of its speed, effectiveness, and low cost. This study developed an MSN agent to mediate and facilitate students' learning in a Web-based course. The students' acceptance of the MSN agent and its effect on learning community identification and learning…
Descriptors: Electronic Learning, Feedback (Response), Web Based Instruction, Identification

Peer reviewed
Direct link
