Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Academic Achievement | 2 |
| Cognitive Processes | 2 |
| Student Motivation | 2 |
| Visual Learning | 2 |
| Artificial Intelligence | 1 |
| Chinese | 1 |
| Cognitive Style | 1 |
| Critical Thinking | 1 |
| Difficulty Level | 1 |
| Elementary School Students | 1 |
| Foreign Countries | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 2 |
| Reports - Research | 2 |
Education Level
| Elementary Education | 1 |
| Grade 5 | 1 |
| Intermediate Grades | 1 |
| Middle Schools | 1 |
| Secondary Education | 1 |
Audience
Location
| China | 1 |
Laws, Policies, & Programs
Assessments and Surveys
| Learning Style Inventory | 1 |
What Works Clearinghouse Rating
Peer reviewed
Direct linkYuchen Chen; Xinli Zhang; Lailin Hu – Educational Technology & Society, 2024
In conventional ancient Chinese poetry learning, students tend to be under-motivated and fail to understand many aspects of poetry. As generative artificial intelligence (GAI) has been applied to education, image-GAI (iGAI) provides great opportunities for students to generate visualized images based on their descriptions of poems, and to situate…
Descriptors: Elementary School Students, Grade 5, Poetry, Artificial Intelligence
Avsec, Stanislav; Szewczyk-Zakrzewska, Agnieszka – International Journal of Technology and Design Education, 2017
This paper aims to investigate the predictive validity of learning styles on academic achievement and technological literacy (TL). For this purpose, secondary school students were recruited (n = 150). An empirical research design was followed where the TL test was used with a learning style inventory measuring learning orientation, processing…
Descriptors: Predictor Variables, Academic Achievement, Technological Literacy, Secondary School Students
