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 |
| Bayesian Statistics | 1 |
| Classification | 1 |
| Computer Games | 1 |
| Data Analysis | 1 |
| Educational Games | 1 |
| Elementary School Students | 1 |
| Feedback (Response) | 1 |
| Formative Evaluation | 1 |
| Grade 5 | 1 |
| Mastery Learning | 1 |
| More ▼ | |
Source
| Journal of Educational Data… | 1 |
Author
| Chen, Fu | 1 |
| Chu, Man-Wai | 1 |
| Cui, Yang | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
| Elementary Education | 1 |
| Grade 5 | 1 |
| Intermediate Grades | 1 |
| Middle Schools | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textCui, Yang; Chu, Man-Wai; Chen, Fu – Journal of Educational Data Mining, 2019
Digital game-based assessments generate student process data that is much more difficult to analyze than traditional assessments. The formative nature of game-based assessments permits students, through applying and practicing the targeted knowledge and skills during gameplay, to gain experiences, receive immediate feedback, and as a result,…
Descriptors: Educational Games, Student Evaluation, Data Analysis, Bayesian Statistics
