Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 1 |
Descriptor
| Accuracy | 1 |
| Artificial Intelligence | 1 |
| Data Analysis | 1 |
| Difficulty Level | 1 |
| Instructional Effectiveness | 1 |
| Perceptual Development | 1 |
| STEM Education | 1 |
| Sequential Learning | 1 |
| Visual Aids | 1 |
| Visual Perception | 1 |
Source
| International Educational… | 1 |
Author
| Mason, Blake | 1 |
| Nowak, Robert | 1 |
| Patel, Purav | 1 |
| Rau, Martina A. | 1 |
| Rogers, Timothy T. | 1 |
| Sen, Ayon | 1 |
| Zhu, Xiaojin | 1 |
Publication Type
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textSen, Ayon; Patel, Purav; Rau, Martina A.; Mason, Blake; Nowak, Robert; Rogers, Timothy T.; Zhu, Xiaojin – International Educational Data Mining Society, 2018
In STEM domains, students are expected to acquire domain knowledge from visual representations that they may not yet be able to interpret. Such learning requires perceptual fluency: the ability to intuitively and rapidly see which concepts visuals show and to translate among multiple visuals. Instructional problems that engage students in…
Descriptors: Visual Aids, Visual Perception, Data Analysis, Artificial Intelligence
