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1. Modeling Memorization: A Data Collection and Mathematical Modeling Experience

   https://doi.org/10.1080/10511970.2022.2068091  

   Tidwell, Will; Anhalt, Cynthia Oropesa; Cortez, Ricardo; Kohler, Brynja R. (January 2022, PRIMUS)

   This article presents a mathematical modeling activity for students related to the process of memorization in which students collect their own data to drive their model development, parameterization, and validation. Engaging in the data collection gives them insight to critique and evaluate various models. This task is a low-floor high ceiling problem that offers both a relatable context and a window to quantitative approaches in cognitive science. Experimental results of students’ participation in this activity are discussed. This article also includes pedagogical recommendations with a focus on fostering equitable teaching practices and a detailed analysis of the situation comprised of several mathematical approaches to model the memorization process that highlight the richness of the problem. Instructors can adapt and implement this modeling exploration for use in various undergraduate courses, from introductory to advanced, depending on the emphasis of the lesson. 

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2. High school students’ data modeling practices and processes: From modeling unstructured data to evaluating automated decisions

   https://doi.org/10.1080/17439884.2023.2189735  

   Jiang, Shiyan; Tang, Hengtao; Tatar, Cansu; Rosé, Carolyn P.; Chao, Jie (January 2023, Learning, Media and Technology)

   It’s critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students’ data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students’ processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies. 

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3. Geophysics-informed hydrologic modeling data [Dataset]

   https://doi.org/10.5281/zenodo.7892511  

   Chen, Hang; Niu, Qifei; Mendieta, Aida; Bradford, John; McNamara, James (January 2023, Zenodo)

   This is for the paper Geophysics-informed hydrologic modeling of a mountain headwater catchment for studying hydrological partitioning in the critical zone. Also check the description in https://github.com/geohang/Geophysics_informed_models 

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4. Modeling subpopulations for hierarchically structured data

   https://doi.org/10.1002/sam.11650  

   Simpson, Andrew; Michael, Semhar; Borchert, Dylan; Saunders, Christopher; Tang, Larry (November 2023, Statistical Analysis and Data Mining: The ASA Data Science Journal)

   Abstract The field of forensic statistics offers a unique hierarchical data structure in which a population is composed of several subpopulations of sources and a sample is collected from each source. This subpopulation structure creates an additional layer of complexity. Hence, the data has a hierarchical structure in addition to the existence of underlying subpopulations. Finite mixtures are known for modeling heterogeneity; however, previous parameter estimation procedures assume that the data is generated through a simple random sampling process. We propose using a semi‐supervised mixture modeling approach to model the subpopulation structure which leverages the fact that we know the collection of samples came from the same source, yet an unknown subpopulation. A simulation study and a real data analysis based on famous glass datasets and a keystroke dynamic typing data set show that the proposed approach performs better than other approaches that have been used previously in practice. 

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5. Data‐driven decision‐focused surrogate modeling

   https://doi.org/10.1002/aic.18338  

   Gupta, Rishabh; Zhang, Qi (January 2024, AIChE Journal)

   Abstract We introduce the concept of decision‐focused surrogate modeling for solving computationally challenging nonlinear optimization problems in real‐time settings. The proposed data‐driven framework seeks to learn a simpler, for example, convex, surrogate optimization model that is trained to minimize thedecision prediction error, which is defined as the difference between the optimal solutions of the original and the surrogate optimization models. The learning problem, formulated as a bilevel program, can be viewed as a data‐driven inverse optimization problem to which we apply a decomposition‐based solution algorithm from previous work. We validate our framework through numerical experiments involving the optimization of common nonlinear chemical processes such as chemical reactors, heat exchanger networks, and material blending systems. We also present a detailed comparison of decision‐focused surrogate modeling with standard data‐driven surrogate modeling methods and demonstrate that our approach is significantly more data‐efficient while producing simple surrogate models with high decision prediction accuracy. 

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