Dreadnought is a free-to-play multiplayer flight simulation in which two teams of 8 players each compete against one another to complete an objective. Each player controls a large-scale spaceship, various aspects of which can be customized to improve a player’s performance in a game. One such aspect is Officer Briefings, which are passive abilities that grant ships additional capabilities. Two of these Briefings, known as Retaliator and Get My Good Side, have strong synergy when used together, which has led to the Dreadnought community’s claiming that the Briefings are too powerful and should be rebalanced to be more in line with the power levels of other Briefings. This study collected gameplay data with and without the use of these specific Officer Briefings to determine the precise impact on gameplay. Linear correlation matrices and inference on two means were used to determine performance impact. It was found that, although these Officer Briefings do improve an individual player’s performance in a game, they do not have a consistent impact on the player’s team performance, and that these Officer Briefings are therefore not in need of rebalancing.

This investigation evaluates the most effective time series model to forecast the stock price for companies that started trading during the COVID-19 stock market crash. My research involved the analysis of five companies in the technology industry. I was able to create three different machine-learning models for each company. Each model contained various criteria to determine the efficacy of the model. The AIC and SBC are common metrics among Autoregressive, autoregressive moving averages, and cross-correlation input models. Lower AIC and SBC values indicated better-fitted models. Additionally, I conducted a white-noise test to determine stationarity. This yielded an Auto-correlation graph determining whether the data was non-stationary or stationary. This paper is supplemented by a project plan, exploratory data analysis, methodology, data, results, and challenges section. This has relevance in understanding the overall stock market trend when impacted by a global pandemic.

This paper recommends amendments to the Montessori teaching system, which can in turn be adapted by individual educators or administrative school boards. The proposed tools mentioned in this paper follow the tenets of Constructivist teaching, which Montessori uses as some of its core teaching values (“Who and What is Montessori?”). Constructivist teaching argues that students learn best when they are able to apply their knowledge base to new learning experiences. The word comes from the idea that students are “constructing” their knowledge base one piece at a time, a process that starts from the ground, or base layer, and builds up from that. This construction involves physical representations of concepts, or guided experiences. Contrary to traditional, “top down” teaching, students learning through constructivist teaching get to experiment with learning concepts before a teacher explains the proper theory. These teachings try to generate excitement for the subject matter as extensions of students’ prior learning. Simulation and data visualization are powerful tools that allow students to discover the patterns present in natural processes by giving them the power to affect the environment and see the results. Implementation of the learning strategies of data visualizations and simulations should improve student performance and excitement in Earth and Space Science (ESS), while also being compliant with the Montessori teaching method.