Hong Jiao
Biographic Data
| ID | 8864617 |
|---|---|
| NAME | Hong Jiao |
| GIVEN NAMES | Hong |
| FAMILY NAME | Jiao |
| SIGNATURE | JIAO H |
| AFFILIATIONS | University of Maryland, College Park, MD, USA |
| ORCID | 0000-0001-5014-6698 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2007 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Item Difficulty Modeling Using Fine-tuned Small and Large Language Models
This study investigates methods for item difficulty modeling in large-scale assessments using both small and large language models (LLMs). We introduce novel data augmentation strategies, including augmentation on the fly and distribution balancing, that surpass benchmark performances, demonstrating their effectiveness in mitigating data imbalance and improving model performance. Our results showed that fine-tuned small language models (SLMs) suc…
Exploration of the Stacking Ensemble Machine Learning Algorithm for Cheating Detection in Large-Scale Assessment
Cheating detection in large-scale assessment received considerable attention in the extant literature. However, none of the previous studies in this line of research investigated the stacking ensemble machine learning algorithm for cheating detection. Furthermore, no study addressed the issue of class imbalance using resampling. This study explored the application of the stacking ensemble machine learning algorithm to analyze the item response, r…
Using the Stan Program for Bayesian Item Response Theory
Stan is a new Bayesian statistical software program that implements the powerful and efficient Hamiltonian Monte Carlo (HMC) algorithm. To date there is not a source that systematically provides Stan code for various item response theory (IRT) models. This article provides Stan code for three representative IRT models, including the three-parameter logistic IRT model, the graded response model, and the nominal response model. We demonstrate how I…
Different Approaches to Covariate Inclusion in the Mixture Rasch Model
The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo simulation study is conducted in which data generated according to a two-class mixture Rasch model with…
Refining the two‐parameter testlet response model by introducing testlet discrimination parameters
For testlet response data, traditional item response theory ( IRT ) models are often not appropriate due to local dependence presented among items within a common testlet. Several testlet‐based IRT models have been developed to model examinees' responses. In this paper, a new two‐parameter normal ogive testlet response theory ( 2PNOTRT ) model for dichotomous items is proposed by introducing testlet discrimination parameters. A Bayesian model par…
Comparison Between Dichotomous and Polytomous Scoring of Innovative Items in a Large-Scale Computerized Adaptive Test
This study explored the impact of partial credit scoring of one type of innovative items (multiple-response items) in a computerized adaptive version of a large-scale licensure pretest and operational test settings. The impacts of partial credit scoring on the estimation of the ability parameters and classification decisions in operational test settings were explored in one real data analysis and two simulation studies when two different polytomo…
Construct Equivalence Across Grades in a Vertical Scale for a K-12 Large-Scale Reading Assessment
In practice, vertical scales have been continually used to measure students’ achievement progress across several grade levels and have been considered very challenging psychometric procedures. Recently, such practices have been drawing many criticisms. The major criticisms focus on dimensionality and construct equivalence of the latent trait or construct of a vertical scale. Using data from a large-scale reading comprehension assessment program w…
Comparability of Computer-Based and Paper-and-Pencil Testing in K–12 Reading Assessments
In recent years, computer-based testing (CBT) has grown in popularity, is increasingly being implemented across the United States, and will likely become the primary mode for delivering tests in the future. Although CBT offers many advantages over traditional paper-and-pencil testing, assessment experts, researchers, practitioners, and users have expressed concern about the comparability of scores between the two test administration modes. To hel…
A Meta-Analysis of Testing Mode Effects in Grade K-12 Mathematics Tests
This study conducted a meta-analysis of computer-based and paper-and-pencil administration mode effects on K-12 student mathematics tests. Both initial and final results based on fixed- and random-effects models are presented. The results based on the final selected studies with homogeneous effect sizes show that the administration mode had no statistically significant effect on K-12 student mathematics tests. Only the moderator variable of compu…
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A Meta-Analysis of Testing Mode Effects in Grade K-12 Mathematics Tests
This study conducted a meta-analysis of computer-based and paper-and-pencil administration mode effects on K-12 student mathematics tests. Both initial and final results based on fixed- and random-effects models are presented. The results based on the final selected studies with homogeneous effect sizes show that the administration mode had no statistically significant effect on K-12 student mathematics tests. Only the moderator variable of compu…
Comparability of Computer-Based and Paper-and-Pencil Testing in K–12 Reading Assessments
In recent years, computer-based testing (CBT) has grown in popularity, is increasingly being implemented across the United States, and will likely become the primary mode for delivering tests in the future. Although CBT offers many advantages over traditional paper-and-pencil testing, assessment experts, researchers, practitioners, and users have expressed concern about the comparability of scores between the two test administration modes. To hel…
Construct Equivalence Across Grades in a Vertical Scale for a K-12 Large-Scale Reading Assessment
In practice, vertical scales have been continually used to measure students’ achievement progress across several grade levels and have been considered very challenging psychometric procedures. Recently, such practices have been drawing many criticisms. The major criticisms focus on dimensionality and construct equivalence of the latent trait or construct of a vertical scale. Using data from a large-scale reading comprehension assessment program w…
Comparison Between Dichotomous and Polytomous Scoring of Innovative Items in a Large-Scale Computerized Adaptive Test
This study explored the impact of partial credit scoring of one type of innovative items (multiple-response items) in a computerized adaptive version of a large-scale licensure pretest and operational test settings. The impacts of partial credit scoring on the estimation of the ability parameters and classification decisions in operational test settings were explored in one real data analysis and two simulation studies when two different polytomo…
Refining the two‐parameter testlet response model by introducing testlet discrimination parameters
For testlet response data, traditional item response theory ( IRT ) models are often not appropriate due to local dependence presented among items within a common testlet. Several testlet‐based IRT models have been developed to model examinees' responses. In this paper, a new two‐parameter normal ogive testlet response theory ( 2PNOTRT ) model for dichotomous items is proposed by introducing testlet discrimination parameters. A Bayesian model par…
Different Approaches to Covariate Inclusion in the Mixture Rasch Model
The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo simulation study is conducted in which data generated according to a two-class mixture Rasch model with…
Using the Stan Program for Bayesian Item Response Theory
Stan is a new Bayesian statistical software program that implements the powerful and efficient Hamiltonian Monte Carlo (HMC) algorithm. To date there is not a source that systematically provides Stan code for various item response theory (IRT) models. This article provides Stan code for three representative IRT models, including the three-parameter logistic IRT model, the graded response model, and the nominal response model. We demonstrate how I…
Exploration of the Stacking Ensemble Machine Learning Algorithm for Cheating Detection in Large-Scale Assessment
Cheating detection in large-scale assessment received considerable attention in the extant literature. However, none of the previous studies in this line of research investigated the stacking ensemble machine learning algorithm for cheating detection. Furthermore, no study addressed the issue of class imbalance using resampling. This study explored the application of the stacking ensemble machine learning algorithm to analyze the item response, r…
Item Difficulty Modeling Using Fine-tuned Small and Large Language Models
This study investigates methods for item difficulty modeling in large-scale assessments using both small and large language models (LLMs). We introduce novel data augmentation strategies, including augmentation on the fly and distribution balancing, that surpass benchmark performances, demonstrating their effectiveness in mitigating data imbalance and improving model performance. Our results showed that fine-tuned small language models (SLMs) suc…
Computer Science (9 works) · Mathematics (8 works) · Psychometric Methodologies and Testing (7 works) · Statistics (7 works) · Psychometrics (6 works) · Psychology (5 works) · Artificial Intelligence (4 works) · Artificial Intelligence (4 works) · Item response theory (4 works) · Machine learning (4 works)