Measuring and Optimizing Influence for Resilient Community Networks

Social networks and the use of technology allow communities to be connected, creating opportunities for individuals to spread information and influence others. This communication is critical when disruptions, such as natural disasters, occur. Finding these influencers, and subsequently maximizing their spread of influence in the network, is key for mitigating the effects of these disasters and restoring communities as quickly as possible. The proposed model seeks to first maximize the spread of influence through the network and then to minimize the vulnerability of the network after the disruption occurs. Maximization of influence involves a mixed integer formulation while minimizing vulnerability requires a bi-level function based on maximizing these influence scores before and after a disruption. The model incorporates social vulnerability scores to ensure the most susceptible members of the community are reached when needed. The network is subjected to disruptions by removing influencers of the community, affecting the most vulnerable members of the population, and creating spatial disruptions to disconnect the network. The model may be used to locate influencers and can be used by decision- makers to determine areas that need more assistance to be resistant to disasters. The model is tested on a sample graph with 16 nodes and applied to a Twitter network to find the influencers before and after a disruption

Adaptive Learning Pedagogy of Universal Design for Learning (UDL) for Multimodal Training

Traditionally, students or trainees usually receive training through a unidirectional instructional approach that can lack interactive activities or through a single material source in classrooms. Therefore, it is possible that some trainees might encounter a sink-or-swim situation if they are not able to understand the materials presented during classroom lectures nor execute correct procedures during laboratory sessions with time-intensive training. To address this issue, the Universal Design for Learning (UDL) asserts that trainees can increase their performance if instructors can provide the trainees with diversified means of information representation, expression opportunities, and engagement means. However, we lack the framework on how to adapt and integrate the process of evaluating the trainees’ learning styles with the UDL principles, especially in the context of time-intensive tasks such as air traffic control training. In this article, we propose an adapted framework that (1) utilizes the Index of Learning Styles (ILS) based on categories such as perception, input, processing, and understanding, (2) maps the UDL methods with the ILS outcomes, and (3) provides possible approaches to address any issues with the teaching materials. The developed approach might be used to investigate whether and how we could enhance the air traffic trainees’ performances at the Federal Aviation Administration (FAA) Academy with minimum need to elongate the training time. The proposed approaches were benchmarked with a small group of qualified Aviation students at the University of Oklahoma who are preparing for the FAA training program to see whether we could find ways to support their learning styles given the time and resource constraints. This preliminary research provides a foundation to improve our approaches when we investigate the learning styles of the trainees’ at the Federal Aviation Administration (FAA) Academy in the near future