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NewGen Psychometrics and Data Science Analytics (NPDA) Lab

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Welcome to NewGen Psychometrics and Data Science Analytics (NPDA) Lab at the University of Georgia! The aim of our research lab is to revolutionize education through the synergistic application of data science, machine learning, and psychometrics in the field of educational measurement. Our multidisciplinary team of researchers from psychometrics, statistics, and educational measurement is dedicated to advancing the understanding of student learning, assessment, and educational outcomes.

We strive to develop innovative methods, tools, and technologies that harness the power of data analytics and predictive modeling to inform evidence-based decision-making in education. By combining cutting-edge techniques from these fields, our research lab seeks to foster improved teaching and learning practices, enhance educational assessment, and ultimately, contribute to the advancement of education on a global scale.

Research Projects

The Item Bank Calibration and Replenishment for Computerized Adaptive Testing in Small Scale Assessments

This project is funded by the National Science Foundation .

  • We will develop a series of statistical estimation methods, theories, algorithms, and software aimed at accelerating the development of CAT and promoting its applications in personalized educational assessment and learning.
  • The project will address a fundamental challenge in educational research: the lack of efficient statistical methods to cope with limited sample sizes and missing data, especially in low-stakes assessment settings.

We are looking for prospective Ph.D. student or a research assistant to join this project in Fall 2024. Please email Shiyu Wang if you are interested in joining this research project.

Intelligent, Adaptive Program with Just-in-Time Feedback for Preservice Teachers

This project is funded by the National Science Foundation.

  • We propose to tailor the interactive, intelligent computer-based program with just-in-time feedback specifically for the needs of middle-grade preservice teachers.
  • We will test whether the program increase preservice teachers’ CK and PCK of ratios and proportional reasoning as well as their teaching of these concepts.
  • This project will provide a proof of concept for developing intelligent computerized programs that use AI technologies to interact with and provide just-in-time feedback for teachers that can be completed by anyone with access to the internet.

We are looking for a prospective Ph.D. student or a research assistant to join this project in Fall 2024. Please email Shiyu Wang or email Allan Cohen if you are interested in joining this research project.

Bayesian Inference for Attribute Hierarchy in Cognitive Diagnosis Models

This project is funded by the National Science Foundation .

  • We have developed Bayesian inference on attribute hierarchy for both static and dynamic CDM models.
  • We aim to promote the use of CDMs in conjunction with attribute hierarchy to facilitate learning.
  • We are in the process of exploring attribute hierarchy and cognitive process learning with multimodal data, such as response accuracy, response times, and eye-tracking data.
Revision and Review Behavior in Large-Scale Computer-Based Assessments: An Analysis of NAEP Mathematics Process Data

This project is funded by NSF-AERA

  • The overall objective of this project is to analyze the recently released NCES restricted-used process, outcome, and survey data from the NAEP 2017 Grade 8 Mathematics Assessment. We plan to attain the overall objective by pursuing four research questions:
  1. How to analyze and interpret unstructured revision and review process data?
  2. Are there latent constructs (i.e. discrete classes or continuous traits) explaining examinees’ patterns of revision and review in large-scale, low-stakes computer-based math assessments?
  3. How do latent constructs for revision/review relate to other important cognitive and noncognitive traits, such as math proficiency and motivation, persistence, and pressure?
  4. How does revision/review behavior relate to demographic and instructional covariates (e.g., student race and ethnicity, disability status, and teacher and school characteristics)?

Faculty Member

Shiyu Wang

Associate professor Shiyu Wang serves as the program chair for the Quantitative Methodology Program in the Department of Educational Psychology at the University of Georgia.

Her research is centered around methodological advancements and innovations across three key domains:

  1. The first area of focus involves the creation of cutting-edge adaptive testing designs. These designs are tailored to offer efficient, personalized assessments hin a testing environment that is user-friendly and accommodating to examinees.

  2. The second facet of her research entails establishing the statistical underpinnings for a family of restricted latent class models. These models serve as a foundation, providing essential guidelines for the estimation and selection of appropriate models.

  3. Wang also focuses on pioneering dynamic psychometric models that can effectively model test-taking and learning process and behaviors.

Her work uses complex multimodal data derived from virtual testing and learning environments. This data encompasses various elements such as response times, log data, textual responses, and eye-tracking data. Beyond her academic pursuits, Wang finds joy in engaging in sports and cherishing quality time with her family.

Visiting Scholar

Xue Wang

Xue Wang is a visiting Ph.D. student in Statistics. She received a bachelor’s degree in mathematics and applied mathematics from Northeast Normal University. She is currently pursuing a combined master’s and doctoral program in Statistics at Northeast Normal University. Her research interests include cognitive diagnosis model, item response theory and Bayesian statistical methodology.

Outside of research, she enjoys listening to music, walking, and jogging.

Ph.D. Students

Tamlyn Lahoud

Tamlyn Lahoud is a Ph.D. student at the University of Georgia in the Quantitative Methodology Program. She completed her master’s in statistics and bachelor’s in mathematical statistics and human kinetics and ergonomics at Rhodes University in South Africa.

Her research interests include item response theory, differential item functioning and topic modeling. Outside of academics, she enjoys rock climbing, traveling, and trying out new recipes.

Yanyan Tan

Yanyan Tan is a doctoral student in the Quantitative Methodology Program. She received her master’s degree in statistics from UGA. Her research interests include diagnostic classification models, learning models, and computerized adaptive testing.

In her free time, she enjoys listening to music, watching movies, walking, and learning new things.

Research Assistants

Eunkyoung Elaine Cha

Eunkyoung Elaine Cha is pursuing a Ph.D. in the Learning, Design, and Technology program, and a M.A. in the Quantitative Methodology program at the University of Georgia. She earned her master’s degree in Educational Studies at the University of Michigan, concentrating on design and technologies for learning across culture and contexts. She graduated from Ewha Womans University in South Korea with a B.A.in Advertisement & Public Relations, double majoring in Television & Film, and minoring in Psychology. Her research interests include technology-assisted language learning, extended reality, multimodality, translanguaging, and language anxiety.

Outside of academics, she enjoys watching movies, listening to music, and playing games.

Cheng Cong

Cheng Cong, a dedicated Ph.D. student in the Department of Statistics who completed his undergraduate studies at the University of Science and Technology of China in 2020. He possesses a fervent enthusiasm for exploring the synergy between statistical methodologies and educational insights.

Aside from his academic life, he enjoys journeying through the fantasy world of Genshin Impact.

Luyang Fang

Luyang Fang is a Ph.D. student in statistics. She received her master’s degree in statistics from the University of Wisconsin-Madison in 2021. Her current research interests encompass educational data mining through the application of statistical methodologies, encompassing non-parametric estimation, subsampling, and deep learning techniques.

Outside of research, she enjoys spending time with family and friends, traveling, and exploring new cities.

Cony Mardones-Segovia

Cony Mardones-Segovia is a Ph.D. candidate in the Quantitative Methodology Program. She received her master’s degree in quantitative methodology from UGA and is a psychologist from the Universidad de Chile.

Her research interests include topic models, mixture models, estimation methods, model selection indices, and diagnostic classification models. Her current work includes investigating the use of machine learning techniques to retrieve more information about examinees thinking from constructed response items.

Aside from her academic life, she enjoys swimming and painting.

Hyunseok Seung

Hyunseok Seung is a Ph.D. student in the Department of Statistics. He earned his bachelor’s and master’s degrees in statistics at Yonsei University in South Korea.

His research focuses on topic modeling, time-series predictions, and optimization in deep learning. He enjoys spending time with his family.

Alumni

Yawei Shen

Yawei Shen recently graduated from the Quantitative Methodology Program with a Ph.D. degree under the supervision of Wang.

Her research interests are computerized adaptive testing and educational data mining. She is currently an associate research scientist at Pearson and works on developing and maintaining state-level linear and adaptive assessments.

In her free time, she enjoys stretching, yoga, Zumba, all types of music, and books. In addition, she loves hot pots, barbecues, coffee, and ice cream.

Shushan Wu

Shushan Wu is a Ph.D. student at UGA in the Department of Statistics. She received her bachelor’s degree in mathematics from Nankai University in China in 2020. Her research interests include geometric machine learning, network data analysis, big data analysis, and applications in smart grid and educational measurement.

In her free time, she likes to hike and read.

Ziwei Zhang

Ziwei Zhang graduated with a master degree from the Quantitative Methodology Program under the supervision of Wang. She currently is a doctoral student in the Quantitative Methods in Education program at the University of Minnesota. Her research interests include intrinsically nonlinear longitudinal models, longitudinal mediation models, and education measurement.

Outside of academics, she enjoys traveling, hiking, boating, pilates, movies, and music.

Collaborative Research Projects

A Multi-Phase Development of the Electric Circuit Concepts Diagnostic Tool

This project is funded by the National Science Foundation .

  • The Electric Circuit Concepts Diagnostic (ECCD) project team will create web-based electric circuit concept inventory that: (1.) provides an immediate and multi-purpose feedback system for reporting about students’ circuits and electricity prior knowledge; (2.) differentiates, with a high probability, between a lack of prior knowledge and misconceptions; and (3.) uses a scheme of multidimensional knowledge profiles to report on students’ prior knowledge and misconceptions.
  • The project team will integrate the affordances of cognitive diagnostic modeling, multi-tier testing frameworks, and computer-assisted testing to realize these project objectives.
The Rational Numbers Playground: Applying and Refining a Model for Dynamic, Discussion-Based Professional Development for Fractions, Ratios, and Proportions

This project is funded by the National Science Foundation.

We are looking for a prospective Ph.D. student or a research assistant to join this project in Fall 2024. Please email Allan Cohen if you are interested in joining this research project.

Resources

Lab Publications   Lab News

Software

Hmcdm R package

This R package can be used for fitting hidden Markov models of learning under the cognitive diagnosis framework. The estimation of the hidden Markov diagnostic classification model, the first order hidden Markov model, the reduced-reparametrized unified learning model, and the joint learning model for responses and response times

Contact Info

Shiyu Wang, associate professor, Department of Educational Psychology
© University of Georgia, Athens, GA 30602
706‑542‑3000