The development of technology allows for the collection of diverse data but also poses challenges in statistical analysis. This research project aims to develop methods for analyzing response process data generated from recent computer-based educational assessments. Such data provide detailed information on test-takers behaviors that traditional item response data cannot capture. However, the complex format of the data and the diversity of human behaviors make it challenging to utilize the information systematically and efficiently. This project will develop innovative methods to understand and identify individual differences in learning and problem-solving. The resulting information will be valuable for designing individualized instruction or intervention strategies to support student success and advocate inclusiveness and equity in education. Additionally, user-friendly software will be developed for practitioners' use, and this project will provide research training opportunities for graduate and undergraduate students. Response process data are an emerging type of data that tracks a respondent's interaction with computer-based items. This project aims to provide innovative, scalable, and interpretable statistical methods for utilizing rich information in response process data. Specifically, this project will focus on developing 1) a data-driven method for extracting features from process data, 2) a latent variable model for understanding how response process dynamics are driven by respondents' latent traits, and 3) a scalar-on-process regression model for describing statistical relationships between response process and other observed variables. Novel computational algorithms will be designed for statistical inference. The strong interpretability of the models will open the black box created by previous machine-learning-based approaches for process data, making it easier to validate the results and gain a deeper understanding of students' problem-solving behaviors. The outcomes of this project will enable educators to better evaluate students and design effective educational strategies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.