Journal Articles

Jiao, H., Yadav*, C., & Li*, G. (2023, May 2). Integrating psychometric analysis and machine learning to augment data for cheating detection in large-scale assessment. Retrieved from psyarxiv.com/fjz2c
Fu, Y., Zhan, P., Chen, Q., & Jiao, H. (2023). Joint Modeling of Action Sequences and Action Times in Problem-solving Tasks. Behavioral Research Methods. https://doi.org/10.3758/s13428-023-02178-2
Zhu, H., Jiao, H., Gao, W., & Meng, X (2023). Bayesian change point analysis approach to detecting aberrant test-taking behavior using response times. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986231151961
Jiao, H., He, Q., & Yao, L. (2023). Machine learning and deep learning in assessment. Psychological Testing and Assessment Modeling.
Yao, L. & Jiao, H. (2023) Comparing Performance of Feature Extraction Methods and Machine Learning Models in Essay Scoring, Chinese/English Journal of Educational Measurement and Evaluation | 教育测量与评估双语期刊: Vol. 4: Iss. 3, Article 1.
https://doi.org/10.59863/DQIZ8440
Zhou*, T. & Jiao, H. (2022). Data augmentation in machine learning for cheating detection: An illustration with the blending learning algorithm. Psychological Testing and Assessment Modeling. 64(4), 425-444.
Qiao, X., Jiao, H., & He, Q. (2022). A multiple group joint modeling of item responses, response times, and action counts with the Conway-Maxwell-Poisson distribution. Journal of Educational Measurement. https://doi.org/10.1111/jedm.12349
Zhou*, T. & Jiao, H. (2022). Exploration of the stacking ensemble machine learning algorithm for cheating detection in large-scale assessment. Educational and Psychological Measuremet.
https://doi.org/10.1177/00131644221117193
Fu, Y., Zhan, P., & Jiao, H. (2022). Joint modeling of action sequences and action times in Problem-solving Tasks. Retrieved from psyarxiv.com/e3nbc
Liao*, M. & Jiao, H. (2022). Modeling multiple problem-solving strategies and strategy shift in cognitive diagnosis for growth. British Journal of Mathematical and Statistical Psychology.
https://doi.org/10.1111/bmsp.12280
Jiao, H. (2022). Comparison of different approaches to dealing with guessing in Rasch modeling. Psychological Testing and Assessment Modeling. 64(1), 65-86.
Qiao*, X. & Jiao, H. (2022). Explanatory cognitive diagnostic modeling incorporating response times. Journal of Educational Measurement.
https://doi.org/10.1111/jedm.12306
Jiao, H, & Liao, M. (2021). Testlet response theory. Educational Measurement: Issues and Practices. https://doi.org/10.1111/emip.12438
Jiao, H., He, Q., & Veldkamp, B. P. (2021). Editorial: Process data in educational and psychological assessment. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2021.793399
Zhan, P., Li, F., & Jiao, H. (2021). Editorial: Cognitive Diagnostic Assessment for Learning. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2021.806636
Wu, F., Yang, X., & Jiao, H. (2021). 基于原则的评估设计——一种测验开发的新取向 [Principled Approaches to Test Development and Design]. Exploring Education Development, 41(10), 30-40.
Zhan, P., Jiao, H, Wang, W. & Man, K. (2021). Variable speed across dimensions of ability in the joint model of responses and response times. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2021.469196
Liao, M., Patton, J., Yan, R., & Jiao, H. (2021). Mining process data to detect aberrant test takers. Measurement: Interdisciplinary Research and Perspectives. 19(2), 93-105. https://doi.org/10.1080/15366367.2020.1827203
Zhan, P., Jiao, H., & Man, K. (2020). The multidimensional log-normal response time model: An exploration of the multidimensionality of latent processing speed. Acta Psychologica Sinica. https://doi.org/10.3724/SP.J.1041.2020.01132
Jiao, H. & Lissitz, R. W. (2020). What hath the coronavirus brought to assessment? Unprecedented challenges in educational assessment in 2020 and years to come. Educational Measurement: Issues and Practice. https://doi.org/10.1111/emip.12363
Liao*, D., He, Q., & Jiao, H. (2019). Mapping background variables with sequential patterns in problem solving environments: An investigation on US adults’ employment status in PIAAC. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2019.00646
Man*, K., Harring, J., Jiao, H., & Zhan*, P. (2019). Conditional joint modeling of compensatory multidimensional item responses and response times. Applied Psychological Measurement. Advanced Online Publication. https://doi.org/10.1177/0146621618824853
Zhan*, P., Jiao, H., Liao*, D. & Li, F (2019). A longitudinal higher-order diagnostic classification model. Journal of Educational and Behavioral Statistics. Advanced Online Publication. https://doi.org/10.3102/1076998619827593
Zhan*, P., Ma, W., Jiao, H., & Ding, S. (2019). A sequential higher-order latent structural model for hierarchical attributes in cognitive diagnostic assessments. Applied Psychological Measurement. Advanced Online Publication. https://doi.org/10.1177/0146621619832935
Zhan*, P., Jiao, H., Man, K, & Wang, L. (2019). Using JAGS for Bayesian cognitive diagnosis modeling: A tutorial. Journal of Educational and Behavioral Statistics. Advanced Online Publication. https://doi.org/10.3102/1076998619826040
Zhan*, P., Wang, W.-C., Jiao, H., & Bian, Y. (2018). The probabilistic-inputs, noisy conjunctive models for cognitive diagnosis. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2018.00997
Zhan*, P., Jiao, H., Liao*, M., & Bian, Y. (2018). Bayesian DINA modeling incorporating within-item characteristics dependency. Applied Psychological Measurement. 43, 143–158. https://doi.org/10.1177/0146621618781594
Qiao*, X., & Jiao, H. (2018). Comparing data mining techniques in analyzing process data: A case study on PISA 2012 problem-solving items. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2018.02231
Zhan*, P. Jiao, H., & Liao*, D. (2017). Cognitive diagnosis modeling incorporating item response times. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12114
Li, Y., Panagiotou, O. A., Black, A., Liao, D., & Wacholder, S. (2016). Multivariate piecewise exponential survival modeling. Biometrics.https://doi.org/10.1111/biom.12435
Luo, Y., & Jiao, H. (2017). Using the Stan program for Bayesian item response theory. Educational and Psychological Measurement. https://doi.org/10.1177/0013164417693666
Li*, T., Xie*, C., & Jiao, H. (2016). Assessing fit of alternative unidimensional polytomous item response models using posterior predictive model checking. Psychological Methods. https://doi.org/10.1037/met0000082
Li*, T., Jiao, H., & Macready, G. (2015). Different approaches to covariate inclusion in the mixture Rasch model. Educational and Psychological Measurement. https://doi.org/10.1177/0013164415610380
Jiao, H., & Zhang*, Y. (2015). Polytomous multilevel testlet models for testlet-based assessments with complex sampling designs. British Journal of Mathematical and Statistical Psychology, 1, 65-83. Online first，https://doi.org/10.1111/bmsp.12035
Wolfe, E., Song, T. W., & Jiao, H. (2015). Features of difficult-to-score essays. Assessing Writing.27, 1-10. https://doi.org/10.1016/j.asw.2015.06.002
Chen*, Y.-F. & Jiao, H. (2014). Exploring the utility of background and cognitive variables in explaining latent differential item functioning: An example of the PISA 2009 reading assessment. Educational Assessment.19, 77-96. https://doi.org/10.1080/10627197.2014.903650.
Jiao, H., Wang, S., & He, W. (2013). Estimation methods for one-parameter testlet models. Journal of Educational Measurement, 50, 186-203. https://doi.org/10.1111/jedm.12010
Wang, S., Jiao, H., & Zhang, L. (2013). Validation of longitudinal achievement constructs of vertically scaled computerized adaptive tests: A multiple-indicator, latent-growth modeling approach. International Journal of Quantitative Research in Education, 1, 383-407. https://doi.org/10.1504/IJQRE.2013.058307
Tao, J., Xu, B., Shi, N., & Jiao, H. (2013). Refining the two-parameter testlet response model by introducing testlet discrimination parameters. Japanese Psychological Research. 55, 284-291
https://doi.org/10.1111/jpr.12002
Wang, S., McCall, M., Jiao, H., & Harris, G. (2013). Construct validity and measurement invariance of computerized adaptive testing: application to Measures of Academic Progress (MAP) using confirmatory factor analysis. Journal of Educational and Developmental Psychology. 3, 88-100. http://dx.doi.org/10.5539/jedp.v3n1p88
Li*, Y., Jiao, H., & Lissitz, R.W. (2012). Applying multidimensional IRT models in validating test dimensionality: An example of K-12 large-scale science assessment. Journal of Applied Testing Technology, vol. 13, n2
Jiao, H., Macready, G., Liu*, J., & Cho*, Y. (2012). A mixture Rasch model based computerized adaptive test for latent class identification. Applied Psychological Measurement, 36, 469-493.https://doi.org/10.1177/0146621612450068
Jiao, H., Kamata, A., Wang, S., & Jin, Y. (2012). A multilevel testlet model for dual local dependence. Journal of Educational Measurement, 49, 82-100. https://doi.org/10.1111/j.1745-3984.2011.00161.x
Jiao, H., Liu*, J., Haynie, K., Woo, A., & Gorham, J. (2012). Comparison between dichotomous and polytomous scoring of innovative items in a large-scale computerized adaptive test. Educational and Psychological Measurement, 72, 493 - 509. https://doi.org/10.1177/0013164411422903
Jiao, H., Lissitz, B., Macready, G., Wang, S., & Liang*, S. (2011). Exploring levels of performance using the Mixture Rasch Model for standard setting. Psychological Testing and Assessment Modeling, 53, 499-522
Jiao, H., & Wang, S. (2010). A multifaceted approach to investigating the equivalence between computer-based and paper-and-pencil assessments: An example of Reading Diagnostics. International Journal of Learning Technology, 5, 264-288.https://doi.org/10.1504/IJLT.2010.037307
Wang, S., & Jiao, H. (2009). Construct equivalence across grades in a vertical scale for a K-12 large-scale reading assessment. Educational and Psychological Measurement, 69, 760-777. https://doi.org/10.1177/0013164409332230
Wang, S., Jiao, H., Young, M. J., Brooks, T., & Olson, J. (2007). Comparability of computer- based and paper-and-pencil testing in K-12 reading assessments: A meta-analysis of testing mode effects. Educational and Psychological Measurement, 68(1), 5-24.https://doi.org/10.1177/0013164407305592
Wang, S., Jiao, H., Young, M. J., Brooks, T., & Olson, J. (2007). A meta-analysis of testing mode effects in Grade K-12 Mathematics Tests. Educational and Psychological Measurement, 67(2), 219-238. https://doi.org/10.1177/0013164406288166
Lissitz, R. W., & Samuelsen, K. (2007). A suggested change in terminology and emphasis regarding validity and education. Educational Researcher, 36, 437-448.https://doi.org/10.3102/0013189X07311286
Lissitz, R. W., & Samuelsen, K. (2007). Further clarification regarding validity and education. Educational Researcher, 36, 482-484. https://doi.org/10.3102/0013189X07311612
Jiao, H., Wang, S., & Kamata, A. (2005). Modeling local item dependence with the hierarchical generalized linear model. Journal of Applied Measurement, 6(3), 311-321.
Wang, S., & Jiao, H. (2005). Development and application of the Stanford achievement test, diagnostic test, and English proficiency test. Examinations Research, 1(1), 118-128. (in Chinese with English abstract)

Maryland Assessment Research Center (MARC)

Journal Articles