More Like this

1. Can Students' Spatial Skills Predict Their Programming Abilities?

   https://doi.org/10.1145/3341525.3387380  

   Bockmon, Ryan; Cooper, Stephen; Gratch, Jonathan; Zhang, Jian; Dorodchi, Mohsen (June 2020, ITiCSE 2020)

   null (Ed.)

   Spatial abilities have been shown to have high predictability in students’ success in STEM related fields. Studies have also shown that there is a correlation between students’ spatial skills and programming abilities, but it is unknown how well students’ prior spatial abilities can predict students’ introductory programming abilities at the end of the semester. During this study we used a multinomal logistic regression to create a predictive model to predict students’ introductory programming abilities at the end of the semester. The highest model accuracy (64.6%) was obtained when accounting for students’ prior programming abilities, prior spatial skills, socioeconomic status, and three factors regarding students’ attitudes towards computing. It was also found that when looking at the predictability of each individual variable, students’ prior spatial ability had the highest predictability (56.6% accuracy) when compared to all other variables. 

   more » « less
2. Spatial Retrievals of Atmospheric Carbon Dioxide from Satellite Observations

   https://doi.org/10.3390/rs13040571  

   Hobbs, Jonathan; Katzfuss, Matthias; Zilber, Daniel; Brynjarsdóttir, Jenný; Mondal, Anirban; Berrocal, Veronica (February 2021, Remote Sensing)

   Modern remote-sensing retrievals often invoke a Bayesian approach to infer atmospheric properties from observed radiances. In this approach, plausible mean states and variability for the quantities of interest are encoded in a prior distribution. Recent developments have devised prior assumptions for the correlation among atmospheric constituents and across observing locations. This work formulates a spatial statistical framework for simultaneous multi-footprint retrievals of carbon dioxide (CO2) with application to the Orbiting Carbon Observatory-2/3 (OCO-2/3). Formally, the retrieval state vector is extended to include atmospheric and surface conditions at many footprints in a small region, and a prior distribution that assumes spatial correlation across these locations is assumed. This spatial prior allows the length-scale, or range, of spatial correlation to vary between different elements of the state vector. Various single- and multi-footprint retrievals are compared in a simulation study. A spatial prior that also includes relatively large prior variances for CO2 results in posterior inferences that most accurately represent the true state and that reduce the correlation in retrieval error across locations. 

   more » « less
3. Spatial adaptation by Bayesian unsupervised trees.

   Liu, Linxi; Ma, Li (December 2024, Proceedings of Machine Learning Research)

   Agrawal, Shipra; Roth, Aaron (Ed.)

   Tree-based methods are popular nonparametric tools for capturing spatial heterogeneity and making predictions in multivariate problems. In unsupervised learning, trees and their ensembles have also been applied to a wide range of statistical inference tasks, such as multi-resolution sketching of distributional variations, localization of high-density regions, and design of efficient data compression schemes. In this paper, we study the spatial adaptation property of Bayesian tree-based methods in the unsupervised setting, with a focus on the density estimation problem. We characterize spatial heterogeneity of the underlying density function by using anisotropic Besov spaces, region-wise anisotropic Besov spaces, and two novel function classes as their extensions. For two types of commonly used prior distributions on trees under the context of unsupervised learning—the optional P{ó}lya tree (Wong and Ma, 2010) and the Dirichlet prior (Lu et al., 2013)—we calculate posterior concentration rates when the density function exhibits different types of heterogeneity. In specific, we show that the posterior concentration rate for trees is near minimax over the anisotropic Besov space. The rate is adaptive in the sense that to achieve such a rate we do not need any prior knowledge of the parameters of the Besov space. 

   more » « less
4. Spatial properties of Bayesian unsupervised trees

   Liu, Linxi; Ma, Li (September 2024, Proceedings of Machine Learning Research)

   Agrawal, Shipra; Roth, Aaron (Ed.)

   Tree-based methods are popular nonparametric tools for capturing spatial heterogeneity and making predictions in multivariate problems. In unsupervised learning, trees and their ensembles have also been applied to a wide range of statistical inference tasks, such as multi-resolution sketching of distributional variations, localization of high-density regions, and design of efficient data compression schemes. In this paper, we study the spatial adaptation property of Bayesian tree-based methods in the unsupervised setting, with a focus on the density estimation problem. We characterize spatial heterogeneity of the underlying density function by using anisotropic Besov spaces, region-wise anisotropic Besov spaces, and two novel function classes as their extensions. For two types of commonly used prior distributions on trees under the context of unsupervised learning—the optional P{ó}lya tree (Wong and Ma, 2010) and the Dirichlet prior (Lu et al., 2013)—we calculate posterior concentration rates when the density function exhibits different types of heterogeneity. In specific, we show that the posterior concentration rate for trees is near minimax over the anisotropic Besov space. The rate is adaptive in the sense that to achieve such a rate we do not need any prior knowledge of the parameters of the Besov space. 

   more » « less
5. STEAM learning in an in-school makerspace: The role of distributed spatial sensemaking.

   Ramey, K. E.; Stevens, R.; Uttal, D. H. (January 2018, Proceedings of the 13th International Conference of the Learning Sciences, London, UK)

   This study examines technology-enhanced STEAM learning among fifth and sixth grade students in one set of in-school makerspaces. It focuses on the learning of one set of meta-disciplinary skills, spatial skills. Prior research has shown these skills to be relevant for STEAM achievement, but they have been underemphasized in our schools and in the literature on learning through making. Informed by a distributed cognition perspective and using a combination of qualitative categorical coding and interaction analysis, this study provides a learning sciences approach to studying spatial thinking and learning. Analyses show that during making activities students engaged in frequent and diverse spatial thinking with a variety of social and material resources and that the sociomaterial contexts of different making activities facilitated different types of spatial thinking. They also show that spatial thinking developed over time and led to problem-solving insights. 

   more » « less