Publication Date
| In 2025 | 0 |
| Since 2024 | 1 |
| Since 2021 (last 5 years) | 3 |
| Since 2016 (last 10 years) | 3 |
| Since 2006 (last 20 years) | 3 |
Descriptor
| Models | 3 |
| Sequential Approach | 3 |
| Accuracy | 2 |
| Classification | 2 |
| Educational Diagnosis | 2 |
| Mastery Learning | 2 |
| Student Evaluation | 2 |
| Ability | 1 |
| Data | 1 |
| Data Analysis | 1 |
| Electronic Learning | 1 |
| More ▼ | |
Author
| A. Corinne Huggins-Manley | 3 |
| Jonathan Templin | 2 |
| Mingying Zheng | 2 |
| Tae Yeon Kwon | 2 |
| Eric A. Wright | 1 |
| M. David Miller | 1 |
| Walter L. Leite | 1 |
| Ziying Li | 1 |
Publication Type
| Reports - Research | 3 |
| Journal Articles | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkTae Yeon Kwon; A. Corinne Huggins-Manley; Jonathan Templin; Mingying Zheng – Journal of Educational Measurement, 2024
In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical…
Descriptors: Classification, Accuracy, Student Evaluation, Sequential Approach
Tae Yeon Kwon; A. Corinne Huggins-Manley; Jonathan Templin; Mingying Zheng – Grantee Submission, 2023
In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical…
Descriptors: Classification, Accuracy, Student Evaluation, Sequential Approach
Download full textZiying Li; A. Corinne Huggins-Manley; Walter L. Leite; M. David Miller; Eric A. Wright – Educational and Psychological Measurement, 2022
The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of…
Descriptors: Sequential Approach, Item Response Theory, Data, Simulation
