Hua Hua Chang
Decision Sciences · Purdue University West Lafayette
Publications
11
Citations
236
Est. group size
—
Recurring co-author estimate
Active years
23
Publishing since 2003
Hua Hua Chang works on the statistical and computational methods behind educational testing and learning technology, including how tests are calibrated and how adaptive or reinforcement-learning-based systems can personalize instruction. This research combines psychometrics (the science of measuring knowledge and ability through tests) with data-driven approaches to make testing and learning systems smarter and more efficient.
Publication output has been sparse and irregular over the past decade, with gaps in several years and only a small uptick in more recent years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Deep Reinforcement Learning for Adaptive Learning Systems
arXiv (Cornell University) · 2020
- A comparison of five methods for pretest item selection in online calibration
International Journal of Quantitative Research in Education · 2017
- A comparison of five methods for pretest item selection in online calibration
International Journal of Quantitative Research in Education · 2017
- From smart testing to smart learning: how testing technology can assist the new generation of education
International Journal of Smart Technology and Learning · 2016
- International Journal of Quantitative Research in Education×2
- arXiv (Cornell University)×2
- International Journal of Smart Technology and Learning×1
- Physical Review Physics Education Research×1
- Journal of Educational Measurement×1
- Dubravka Svetina
Decision Sciences · Indiana University
- Hua‐Hua Chang
Decision Sciences · Purdue University West Lafayette
- Paul De Boeck
Decision Sciences · The Ohio State University
- María Elena Oliveri
Decision Sciences · Purdue University West Lafayette
- Sijia Huang
Decision Sciences · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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