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In monitoring station observation, for the best accuracy of rumor source detection, it is important to deploy monitors appropriately into the network. There are, however, a very limited number of studies on the monitoring station selection. This article will study the problem of detecting a single rumormonger based on an observation of selected infection monitoring stations in a complete snapshot taken at some time in an online social network (OSN) following the independent cascade (IC) model. To deploy monitoring stations into the observed network, we propose an influence-distancebased k-station selection method where the influence distance is a conceptual measurement that estimates the probability that a rumor-infected node can influence its uninfected neighbors. Accordingly, a greedy algorithm is developed to find the best k monitoring stations among all rumor-infected nodes with a 2-approximation. Based on the infection path, which is most likely toward the k infection monitoring stations, we derive that an estimator for the “most like” rumor source under the IC model is the Jordan infection center in a graph. Our theoretical analysis is presented in the article. The effectiveness of our method is verified through experiments over both synthetic and real-world datasets. As shown in the results, our k-station selection method outperforms off-the-shelf methods in most cases in the network under the IC model. 

A usability study evaluated the ease with which users interacted with an author-designed modeling and simulation program called STEPP (Scaffolded Training Environment for Physics Programming). STEPP is a series of educational modules for introductory algebra-based physics classes that allow students to model the motion of an object using Finite State Machines (FSMs). STEPP was designed to teach students to decompose physical systems into a few key variables such as time, position, and velocity and then encourages them to use these variables to define states (such as running a marathon) and transitions between these states (such as crossing the finish line). We report the results of a usability study on high school physics teachers that was part of a summer training institute. To examine this, 8 high school physics teachers (6 women, 2 men) were taught how to use our simulation software. Data from qualitative and quantitative measures revealed that our tool generally exceeded teacher’s expectations across questions assessing: (1) User Experience, (2) STEM-C Relevance, and (3) Classroom Applicability. Implications of this research for STEM education and the use of modeling and simulation to enhance sustainability in learning will be discussed.