Hua-Hua Chang
Biographic Data
| ID | 7692230 |
|---|---|
| NAME | Hua-Hua Chang |
| GIVEN NAMES | Hua-Hua |
| FAMILY NAME | Chang |
| SIGNATURE | CHANG H |
| AFFILIATIONS | University of Illinois Urbana-Champaign |
| ORCID | 0000-0002-2949-1902 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2003 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
The Application of Machine Learning to Educational Process Data Analysis
Educational process data offers valuable opportunities to enhance teaching and learning by providing more detailed insights into students’ learning and problem-solving processes. However, its large size, unstructured format, and inherent noise pose significant challenges for effective analysis. Machine learning (ML) has emerged as a powerful tool for tackling such complexities. Despite growing interest, a comprehensive review of ML applications i…
Development and techniques in learner model in adaptive e-learning system
A comparative study on cognitive diagnostic assessment of mathematical key competencies and learning trajectories
Item Selection Criteria With Practical Constraints in Cognitive Diagnostic Computerized Adaptive Testing
For item selection in cognitive diagnostic computerized adaptive testing (CD-CAT), ideally, a single item selection index should be created to simultaneously regulate precision, exposure status, and attribute balancing. For this purpose, in this study, we first proposed an attribute-balanced item selection criterion, namely, the standardized weighted deviation global discrimination index (SWDGDI), and subsequently formulated the constrained progr…
Item Selection for the Development of Parallel Forms From an IRT-Based Seed Test Using a Sampling and Classification Approach
Two sampling-and-classification–based procedures were developed for automated test assembly: the Cell Only and the Cell and Cube methods. A simulation study based on a 540-item bank was conducted to compare the performance of the procedures with the performance of a mixed-integer programming (MIP) method for assembling multiple parallel test forms. The study investigated the statistical equivalence of the forms generated by the three test assembl…
Content-balancing strategy in bifactor computerized adaptive patient-reported outcome measurement
Constraint-Weighted a -Stratification for Computerized Adaptive Testing With Nonstatistical Constraints
a-stratification is a method that utilizes items with small discrimination (a) parameters early in an exam and those with higher a values when more is learned about the ability parameter. It can achieve much better item usage than the maximum information criterion (MIC). To make a-stratification more practical and more widely applicable, a method for weighting the item selection process in a-stratification as a means of satisfying multiple test c…
Incorporation Of Content Balancing Requirements In Stratification Designs For Computerized Adaptive Testing
In computerized adaptive testing, the multistage a-stratified design advocates a new philosophy on pool management and item selection in which, contradictory to common practice, less discriminating items are used first. The method is effective in reducing item-overlap rate and enhancing pool utilization. This stratification method has been extended in different ways to deal with the practical issues of content constraints and the positive correla…
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Incorporation Of Content Balancing Requirements In Stratification Designs For Computerized Adaptive Testing
In computerized adaptive testing, the multistage a-stratified design advocates a new philosophy on pool management and item selection in which, contradictory to common practice, less discriminating items are used first. The method is effective in reducing item-overlap rate and enhancing pool utilization. This stratification method has been extended in different ways to deal with the practical issues of content constraints and the positive correla…
Constraint-Weighted a -Stratification for Computerized Adaptive Testing With Nonstatistical Constraints
a-stratification is a method that utilizes items with small discrimination (a) parameters early in an exam and those with higher a values when more is learned about the ability parameter. It can achieve much better item usage than the maximum information criterion (MIC). To make a-stratification more practical and more widely applicable, a method for weighting the item selection process in a-stratification as a means of satisfying multiple test c…
Item Selection for the Development of Parallel Forms From an IRT-Based Seed Test Using a Sampling and Classification Approach
Two sampling-and-classification–based procedures were developed for automated test assembly: the Cell Only and the Cell and Cube methods. A simulation study based on a 540-item bank was conducted to compare the performance of the procedures with the performance of a mixed-integer programming (MIP) method for assembling multiple parallel test forms. The study investigated the statistical equivalence of the forms generated by the three test assembl…
Content-balancing strategy in bifactor computerized adaptive patient-reported outcome measurement
Item Selection Criteria With Practical Constraints in Cognitive Diagnostic Computerized Adaptive Testing
For item selection in cognitive diagnostic computerized adaptive testing (CD-CAT), ideally, a single item selection index should be created to simultaneously regulate precision, exposure status, and attribute balancing. For this purpose, in this study, we first proposed an attribute-balanced item selection criterion, namely, the standardized weighted deviation global discrimination index (SWDGDI), and subsequently formulated the constrained progr…
A comparative study on cognitive diagnostic assessment of mathematical key competencies and learning trajectories
The Application of Machine Learning to Educational Process Data Analysis
Educational process data offers valuable opportunities to enhance teaching and learning by providing more detailed insights into students’ learning and problem-solving processes. However, its large size, unstructured format, and inherent noise pose significant challenges for effective analysis. Machine learning (ML) has emerged as a powerful tool for tackling such complexities. Despite growing interest, a comprehensive review of ML applications i…
Development and techniques in learner model in adaptive e-learning system
Computer Science (6 works) · Mathematics (5 works) · Statistics (5 works) · Computerized adaptive testing (4 works) · Psychometric Methodologies and Testing (4 works) · Psychometrics (4 works) · Advanced Statistical Modeling Techniques (3 works) · Artificial Intelligence (3 works) · Artificial Intelligence (3 works) · Selection (genetic algorithm) (3 works)