This research study investigates the design principles and best practices for incorporating gamification in EduMobile apps for teaching about mosquito breeding grounds. With limited research investigating the effectiveness of EduMobile apps in engaging and educating students on complex topics, this study aims to uncover best practices for designing EduMobile apps for early learners (elementary and middle schoolers). A convenience sample of adults who were not part of the target demographic were recruited to test the app. The System Usability Scale was used to measure user satisfaction, and question-wise t-tests were conducted to analyze the effectiveness of specific design changes. Results show a significant difference in user satisfaction between the original and revised designs, with question 5 of the System Usability Scale driving the overall difference in score. Inconsistent design was found to increase extraneous cognitive load and split attention, while consistency within different views was shown to increase user perception of system integration. These findings suggest that incorporating gamification and following best practices in designing EduMobile apps can increase student engagement and motivation in learning about mosquito breeding grounds.

The purpose of this research thesis paper is to provide further insight into the development of extended reality (XR), augmented reality (AR), and virtual reality (VR) technologies within the educational space and survey how well they are received as well as whether or not they can provide additional learning benefit in regards to other learning mediums such as reading textbooks, watching videos on the subject matter, and other such more traditional mediums. The research conducted consisted of a collaborative effort alongside the School of Biological and Health Systems Engineering (SBHSE) personnel and using their provided resources in order to generate a framework with the aforementioned technology, to aid in the development of a web-based XR system which will serve primarily as a means for SBHSE students at Arizona State University (ASU) to enhance their learning experience when it comes to topics such as anatomy and physiology of the human body, with the potential of extending this technology towards other subject matters as well, such as other STEM-related fields. Information about the initial research which included an analysis of the pertinent readings that support a benefit to using XR technology as a means to deliver course content is what is first focused on throughout this document. Then, the process that went into the design and development of the base framework that was in joint collaboration with the SBHSE will be covered. And, to conclude, a case study to generate applicable data to support the argument is covered as well as the results from it, which presented a potential for a future development plan and next steps plan once the developed materials and research are handed off.

This paper explores the inner workings of algorithms that computers may use to play Chess. First, we discuss the classical Alpha-Beta algorithm and several improvements, including Quiescence Search, Transposition Tables, and more. Next, we examine the state-of-the-art Monte Carlo Tree Search algorithm and relevant optimizations. After that, we consider a recent algorithm that transforms Alpha-Beta into a “Rollout” search, blending it with Monte Carlo Tree Search under the rollout paradigm. We then discuss our C++ Chess Engine, Homura, and explain its implementation of a hybrid algorithm combining Alpha-Beta with MCTS. Finally, we show that Homura can play master-level Chess at a strength currently exceeding that of our backtracking Alpha-Beta.

The purpose of this thesis is to contextualise Hindsight, a sustainability-focused historically based city-simulation and resource management game built by the author. The game and game engine were coded from scratch using the C# programming language and the Unity game development suite of tools. The game focuses on the management of the city of London in two time periods, London from 1850 and the other set in 2050. Both versions of the city are divided into 21 zones, each of which can be managed by the player through the construction, upgrading, or destruction of various buildings within the zone. The player must manage both the city’s resources and the resources of the environment upon which the city depends in order to bring about a more sustainable future and bring the 2050-era version of the city back from the brink of environmental devastation. Along the way, the player must address the cultural views of the society they are managing to ensure their reforms will be accepted and can also see those views slowly change over time. The goal of the game is to provide an interactive learning experience for both the historical element of London and the importance of making sustainable choices.

A Skunkworks project is the name given to a small team of individuals leading an innovative undertaking, and conducting research and development outside of the normal scope of an organization. With this concept in mind, our team of six individuals was tasked with finding and conceptualizing innovative solutions within varying business markets of interest. Our team started off with five markets that we identified issues in and were passionate about solving. These included Sports Engagement, Education, Student Debt, Digital Literacy, and Viral Health. From extensive research, trial and error, and endless conversations we settled on creating business models in two final areas: Student Debt and Viral Health. Our research in Student Debt led us to the discovery that the average Arizona State student, takes out $21,237 in loans for their four year degree and in the whole state of Arizona, a student takes on an average of $22,253. Our solution to this problem was to create a student financial app that served as an efficient debt tracker that provided important information about finances, investing, and student loan information. Additionally, our team also wanted the address the issue of sexually transmitted diseases, just a small scope of Viral Health, within Arizona State University. Our research led us to discover that 50% of people report not getting tested, and from this population most reported it was due to anxiety and financial issues. From our research the StayInformed app was created to provide students with better accessibility to both at-home and clinic testing services, and updated education on sexual health. With this project model we hope to increase the rate of students testing and allow students more agency over their sexual health. Although these two services are addressing very different markets, they both utilize forward thinking technology to create much needed solutions and better the lives of students.

Machine learning is a rapidly growing field, with no doubt in part due to its countless applications to other fields, including pedagogy and the creation of computer-aided tutoring systems. To extend the functionality of FACT, an automated teaching assistant, we want to predict, using metadata produced by student activity, whether a student is capable of fixing their own mistakes. Logs were collected from previous FACT trials with middle school math teachers and students. The data was converted to time series sequences for deep learning, and ordinary features were extracted for statistical machine learning. Ultimately, deep learning models attained an accuracy of 60%, while tree-based methods attained an accuracy of 65%, showing that some correlation, although small, exists between how a student fixes their mistakes and whether their correction is correct.

Augmented Reality (AR) especially when used with mobile devices enables the creation of applications that can help students in chemistry learn anything from basic to more advanced concepts. In Chemistry specifically, the 3D representation of molecules and chemical structures is of vital importance to students and yet when printed in 2D as on textbooks and lecture notes it can be quite hard to understand those vital 3D concepts. ARsome Chemistry is an app that aims to utilize AR to display complex and simple molecules in 3D to actively teach students these concepts through quizzes and other features. The ARsome chemistry app uses image target recognition to allow students to hand-draw or print line angle structures or chemical formulas of molecules and then scan those targets to get 3D representation of molecules. Students can use their fingers and the touch screen to zoom, rotate, and highlight different portions of the molecule to gain a better understanding of the molecule's 3D structure. The ARsome chemistry app also features the ability to utilize image recognition to allow students to quiz themselves on drawing line-angle structures and show it to the camera for the app to check their work. The ARsome chemistry app is an accessible and cost-effective study aid platform for students for on demand, interactive, 3D representations of complex molecules.