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Spatial abilities have been found to interact with the design of visualizations in educational materials in different forms: (1) spatial abilities enhanced learning with optimized visual design (ability-as-enhancer) or (2) spatial abilities compensated for suboptimal visual design (ability-as-compensator). A brief review of pertinent studies suggests that these two forms are viewed as mutually exclusive. We propose a novel unifying conceptualization. This conceptualization suggests that the ability-as enhancer interaction will be found in the low-medium range of a broad ability continuum whereas the ability-as-compensator interaction will be found in the medium-high range. The largest difference in learning outcomes between visual design variations is expected for medium ability. A corresponding analytical approach is suggested that includes nonlinear quadratic interactions. The unifying conceptualization was confirmed in an experiment with a consistent visual-spatial task. In addition, the conceptualization was investigated with a reanalysis of pooled data from four multimedia learning experiments. Consistent with the conceptualization, quadratic interactions were found, meaning that interactions depended on ability range. The largest difference between visual design variations was obtained for medium ability, as expected. It is concluded that the unifying conceptualization is a useful theoretical and methodological approach to analyze and interpret aptitude-treatment interactions that go beyond linear interactions.
Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.