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Learning to derive subgoals reduces the gap between experts and students and makes students prepared for future problem solving. Researchers have explored subgoal-labeled instructional materials in traditional problem solving and within tutoring systems to help novices learn to subgoal. However, only a little research is found on problem-solving strategies in relationship with subgoal learning. Also, these strategies are under-explored within computer-based tutors and learning environments. The backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty. In this paper, we explore a training strategy for backward strategy learning within an intelligent logic tutor that teaches logic-proof construction. The training session involved backward worked examples (BWE) and problem solving (BPS) to help students learn backward strategy towards improving their subgoaling and problem-solving skills. To evaluate the training strategy, we analyzed students’ 1) experience with and engagement in learning backward strategy, 2) performance and 3) proof construction approaches in new problems that they solved independently without tutor help after each level of training and in posttest. Our results showed that, when new problems were given to solve without any tutor help, students who were trained with both BWE and BPS outperformed students who received none of the treatment or only BWE during training. Additionally, students trained with both BWE and BPS derived subgoals during proof construction with significantly higher efficiency than the other two groups.

 

Our goal in this work is to build effective yet robust models to predict unreliable and inconsistent in-kind donations at both weekly and monthly levels for two food banks across coasts: the Food Bank of Central Eastern North Carolina in North Carolina and Los Angeles Regional Food Bank in California. We explore three factors: model, data length, and window type. For the model, we evaluate a series of classic time-series forecasting models against the state-of-the-art approaches such as Bayesian Structural Time Series modeling (BSTS) and deep learning models; for the data length, we vary training data from 2 weeks to 13 years; for the window type, we compare sliding vs. expanding. Our results show the effectiveness of different models heavily depends on the data length and the window type as well as characteristics of the food bank. Motivated by these findings, we investigate the effectiveness of employing an average of all predictions formed by considering all three factors at both monthly and weekly levels for both food banks. Our results show that this average of predictions significantly and consistently outperforms all classical models, deep learning, and BSTS for the donation prediction at both monthly and weekly levels for both food banks. 

It is shown that appropriate therapeutic management at early stages of sepsis are crucial for preventing further deterioration and irreversible organ damage. Although previous studies considered the cellular and physiological responses as the components of sepsis-related predictive models, temporal connections among the responses have not been widely studied. The objective of this study is to investigate simultaneous changes in cellular and physiological responses represented by 16 clinical variables contributing to seven organ system dysfunctions in patients with sepsis to predict in-hospital mortality. Organ dysfunctions were represented by undirected weighted network models composed of: i) nodes (i.e., 16 clinical variables and three biomarkers including procalcitonin, C-reactive protein, and sedimentation rate), ii) edges (i.e., connection between pair of nodes representing simultaneous dysfunctions), and iii) weights representing the persistence of the co-occurrence of two dysfunctions. Data was collected from 13,367 adult patients (corresponding to 17,953 visits) admitted to the study hospital from July 1, 2013, to December 31, 2015. The study population were categorized based on clinical criteria representing sepsis progression to identify different subpopulations. The findings quantify the optimal window for defining the simultaneity of two dysfunctions, the network properties corresponding to different subpopulations, the discriminatory patterns of simultaneous dysfunctions among subpopulations and in-hospital mortality prediction. The results show that the level of persistence of simultaneous dysfunctions are subpopulation-specific. Insights from this study regarding optimal thresholds of the persistence and combination of simultaneous organ dysfunctions can inform policies to personalize the in-hospital mortality prediction. 

Marine aerosols strongly influence climate through their interactions with solar radiation and clouds. However, significant questions remain regarding the influences of biological activity and seawater chemistry on the flux, chemical composition, and climate-relevant properties of marine aerosols and gases. Wave channels, a traditional tool of physical oceanography, have been adapted for large-scale ocean-atmosphere mesocosm experiments in the laboratory. These experiments enable the study of aerosols under controlled conditions which isolate the marine system from atmospheric anthropogenic and terrestrial influences. Here, we present an overview of the 2019 Sea Spray Chemistry and Particle Evolution (SeaSCAPE) study, which was conducted in an 11 800 L wave channel which was modified to facilitate atmospheric measurements. The SeaSCAPE campaign sought to determine the influence of biological activity in seawater on the production of primary sea spray aerosols, volatile organic compounds (VOCs), and secondary marine aerosols. Notably, the SeaSCAPE experiment also focused on understanding how photooxidative aging processes transform the composition of marine aerosols. In addition to a broad range of aerosol, gas, and seawater measurements, we present key results which highlight the experimental capabilities during the campaign, including the phytoplankton bloom dynamics, VOC production, and the effects of photochemical aging on aerosol production, morphology, and chemical composition. Additionally, we discuss the modifications made to the wave channel to improve aerosol production and reduce background contamination, as well as subsequent characterization experiments. The SeaSCAPE experiment provides unique insight into the connections between marine biology, atmospheric chemistry, and climate-relevant aerosol properties, and demonstrates how an ocean-atmosphere-interaction facility can be used to isolate and study reactions in the marine atmosphere in the laboratory under more controlled conditions.