This 15-week long course is designed to introduce students, specifically in Arizona, to basic sustainability and conservation principles in the context of local reptile wildlife. Throughout the course, the students work on identifying the problem, creating visions for the desired future, and finally developing a strategy to help with reptile species survival in the valley. Research shows that animals in the classroom have led to improved academic success for students. Thus, through creating this course I was able to combine conservation and sustainability curriculum with real-life animals whose survival is directly being affected in the valley. My hope is that this course will help students identify a newfound passion and call to action to protect native wildlife. The more awareness and actionable knowledge which can be brought to students in Arizona about challenges to species survival the more likely we are to see a change in the future and a stronger sense of urgency for protecting wildlife. In order to accomplish these goals, the curriculum was developed to begin with basic concepts of species needs such as food and shelter and basic principles of sustainability. As the course progresses the students analyze current challenges reptile wildlife faces, like urban sprawl, and explore options to address these challenges. The course concludes with a pilot pitch where students present their solution projects to the school.

The concept of Nature Made Candles was to “educate candle lovers on the importance of knowing what is in the candle. Everyone should know what they are inhaling...no matter how nice (or not) it smells. Earth needed a candle for enjoying scents and sights without hindering health, so we made one.” The objective evolved into educating the student population of Arizona State University (ASU) about what ingredients go into commercial candles, with a particular focus on the wax and scent, as well as giving students a free candle that emulated the holistic ingredients they were educated on. This project was designed to be a quality improvement and health promotion project with an emphasis on the ASU student population. The purpose of the project was to find a type of candle that was friendly to the lungs of all individuals who wanted candles in their household.

Though about 75 percent of American waste is recyclable, only 30 percent of it is actually recycled and less than ten percent of plastics disposed of in the United States in 2015 were recycled. A statistic like this demonstrates the immense need to increase recycling rates in order to move towards cultivating a circular economy and benefiting the environment. With Arizona State University’s (ASU) extensive population of on-campus students and faculty, our team was determined to create a solution that would increase recycling rates. After conducting initial market research, our team incentives or education. We conducted market research through student surveys to determine the level of knowledge of our target audience and barriers to entry for local recycling and composting resources. Further, we gained insight into the medium of recycling and sustainability programs they would be interested in participating in. Overall, the results of our surveys demonstrated that a majority of students were interested in participating in these programs, if they were not already involved, and most students on-campus already had access to these resources. Despite having access to these sustainable practices, we identified a knowledge gap between students and their information on how to properly execute sustainable practices such as composting and recycling. In order to address this audience, our team created Circulearning, an educational program that aims to bridge the gap of knowledge and address immediate concerns regarding circular economy topics. By engaging audiences through our quick, accessible educational modules and teaching them about circular practices, we aim to inspire everyone to implement these practices into their own lives. Though our team began the initiative with a focus on implementing these practices solely to ASU campus, we decided to expand our target audience to implement educational programs at all levels after discovering the interest and need for this resource in our community. Our team is extremely excited that our Circulearning educational modules have been shared with a broad audience including students at Mesa Skyline High School, ASU students, and additional connections outside of ASU. With Circulearning, we will educate and inspire people of all ages to live more sustainably and better the environment in which we live.

The academic environment has historically been somewhat slow to implement and adopt new technologies. However, developments in video games have created an opportunity for students to learn new skills and topics through nontraditional mediums of education. The disruption caused by the COVID-19 pandemic further highlighted the need for flexible learning opportunities. Joystick Education is our approach to addressing this need. Through online, game-based tutoring and a database of video games with high educational value, Joystick Education creates a learning environment that is effective, fun, and engaging for students. We analyzed popular, mainstream video games for educational content and selected nine games that teach concepts like history, biology, or physics while playing the game. Through promotion on social media, we generated buzz around our website which led to 103 unique visitors over our first month online and two customers requesting to book our tutoring service. We are confident that given more time to grow, Joystick Education can generate profit and become a successful business.

When earning a teaching certification, there is no curriculum when it comes to the treatment of students with a diagnosis as well as how to educate their fellow classmates. Diagnoses affect the process of child development of the diagnosed as well as the friends and family. Children of all different ages have different responses and reactions to the world of health. Looking at a developmental perspective, teachers can properly educate themselves and their students about these diagnoses. To be able to successfully inform students of diagnoses, there must be an overall understanding of how well they are able to acquire the knowledge. According to Jean Piaget, a key researcher in cognitive development, the age of the child correlates with their overall understanding and comprehension. In his theory, he explained how he believed that the environment of an organism affects how it will respond and adapt to the situations at hand. There are four stages that are connected to age, from infancy to adolescence and adulthood. Therefore, this project will focus on school-age children who are in the concrete operational stage. The concrete operational stage is made up of elementary and early adolescents and focuses on intelligence that is demonstrated through logical and precise thinking of concrete ideas (Huitt, W., & Hummel, J, 2003). This type of thinking applies to all parts of the child’s life and informs their behaviors on how to “adapt” to new information. Knowing this information, we will be able to create a curriculum of lectures, informational videos, worksheets and quizzes that can properly assess the student’s and their knowledge of the diagnoses.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

Though about 75 percent of American waste is recyclable, only 30 percent of it is actually recycled and less than ten percent of plastics disposed of in the United States in 2015 were recycled. A statistic like this demonstrates the immense need to increase recycling rates in order to move towards cultivating a circular economy and benefiting the environment. With Arizona State University’s (ASU) extensive population of on-campus students and faculty, our team was determined to create a solution that would increase recycling rates. After conducting initial market research, our team incentives or education. We conducted market research through student surveys to determine the level of knowledge of our target audience and barriers to entry for local recycling and composting resources. Further, we gained insight into the medium of recycling and sustainability programs they would be interested in participating in. Overall, the results of our surveys demonstrated that a majority of students were interested in participating in these programs, if they were not already involved, and most students on-campus already had access to these resources. Despite having access to these sustainable practices, we identified a knowledge gap between students and their information on how to properly execute sustainable practices such as composting and recycling. In order to address this audience, our team created Circulearning, an educational program that aims to bridge the gap of knowledge and address immediate concerns regarding circular economy topics. By engaging audiences through our quick, accessible educational modules and teaching them about circular practices, we aim to inspire everyone to implement these practices into their own lives. Though our team began the initiative with a focus on implementing these practices solely to ASU campus, we decided to expand our target audience to implement educational programs at all levels after discovering the interest and need for this resource in our community. Our team is extremely excited that our Circulearning educational modules have been shared with a broad audience including students at Mesa Skyline High School, ASU students, and additional connections outside of ASU. With Circulearning, we will educate and inspire people of all ages to live more sustainably and better the environment in which we live.

Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

Brundtland’s definition of sustainability is the ability to “meet the needs of the present<br/>without compromising the ability of future generations to meet their needs” (IISD, 2021). But<br/>what if there are no future generations? Social sustainability, the sector of sustainability that<br/>foregrounds the well-being and livelihoods of people (and thereby continuation of humanity), is<br/>included in definitions within the sustainability field, but less developed in sustainability<br/>practice. In an effort to bridge this gap of knowledge, 14 U.S. cities and over 100 sustainability<br/>policies were analyzed for their social sustainability performance. An eight-item analytical<br/>framework that deals with differing areas of social equity guided the analysis. Results found that<br/>most cities’ sustainability departments fell short of truly addressing social sustainability<br/>concerns. Out of the eight items, the most frequently addressed were housing security and racial<br/>and gender equality whereas few, if any, cities addressed the more specific social concerns of<br/>immigration, technology and media, or arts/cultural preservation. Future research is<br/>recommended to gain a better understanding of the ways existing cities can improve in this area.