Anxiety related to academics, especially with regards to testing, is a rapidly growing mental health issue impacting all undergraduates at TCU. High levels of test anxiety have been shown to inhibit memory recall, reduce exam scores, and promote poor study habits. Expressive writing is a form of test anxiety intervention consisting of a type of free response developed to allow students to release their minds of anxious thoughts and emotions related to exams. Expressive writing has been shown to reduce test anxiety and improve exam performance. However, the effectiveness of expressive writing may be mitigated by a students’ level of emotional intelligence. Emotional intelligence is defined as the capacity to be aware of, control, and express one’s emotions. In order to better understand the connection between emotional intelligence and expressive writing, I conducted an exploratory mixed-methods study using quantitative survey data to inform our selection of interview participants and the initial development of my interview protocol. Initially, to gain insight into students’ responses to the expressive writing prompt, I collected and coded expressive writing samples from GEOL 10113 students during the Fall of 2020. Prior to the beginning of the semester, I asked the students to complete a Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) to assign a numerical value to their approximate emotional intelligence levels. Next, I stratified students by quartiles into high (Q3), medium (Q2), and low (Q1) emotionally intelligent groups. Then, GEOL 10113 students were asked to participate in focus group interviews, and volunteers were subsequently grouped by their TEIQue-SF score into three focus groups relating to the high, medium, and low emotional intelligence bins. Finally, using the focus group interview data and the selected student’s responses from the initial expressive writing exercise, I found that all of the student responses showed consistent differences between the three focus groups. Highly emotionally intelligent students had more positive experiences with the expressive writing exercise while lower emotionally intelligent students tended to have neutral or negative experiences with the activity. Overall, these data suggest that the level of emotional intelligence relates to how willing students were to convey their thoughts and emotions during the activity, which helped the higher emotionally intelligent students to have positive experiences with the writing exercise. The implications of my study are that while the efficacy of the expressive writing exercise is assumed, emotional intelligence is a confounding variable. Students must engage with it in some sort of authentic manner if they are to benefit from the exercise.

Solar energy is a significant contributor to the renewable energy mix. Many urban developments are making investments to install solar systems across feasible areas. The allocation of solar systems relies on the land’s geography and the amount of solar radiation received. The purpose of this study is to apply to determine the best sites for solar installations in urban areas. Using the TCU area of Fort Worth, Texas as a case study, this study will use ESRI’s ArcMap and ArcGIS Pro to estimate the solar power potential of different residential rooftops. The results will be useful in showing what households are most suitable for solar installation based on their expected energy yield.

Rapid industrialization and global population growth have increased the number of people living in urban areas worldwide. Developing countries, have seen tremendous increases in their industries over the past decades, which generated both positives and negative effects on their people, environment, and economy. One of the negative impacts of industrialization is industrial pollution and the increase in the number of pollutants released into the environment_ in this case, heavy metals. Heavy metal contamination is an alarming problem that many Developing countries are becoming aware of and trying to address. Heavy metal direct or indirect consumption may result in several health effects in the body, including damage and alteration of normal functioning of organs such as the brain, kidney, lungs, liver, and blood, which later result into acute or chronic diseases. This case study will look at heavy metal contamination cases in Rwanda in different drinking water sources. The focus of this case study will be on some common heavy metals released from industrial waste: Lead, Manganese, Iron, Cadmium, Zinc, and Chromium.

Teachers have experimented with the idea of virtual learning and its’ effects on student achievement. Due to the coronavirus pandemic, many schools and universities transitioned from traditional classroom-focused learning to asynchronous online learning. Asynchronous online learning is a type of instruction where online learning is not happening at the same time or place. Consequently, TCU made the abrupt transition in the Spring of 2021 to fully online asynchronous courses. To understand the magnitude of how remote learning can effect students’ academic success, my research project looks at what factors, including remote learning, can predict final grade utilizing GEOL 10113 student performance data and survey data from the spring semester of 2020 surveys. To investigate the impact of online learning, I tested several linear models to determine what confounders have a significant role in predicting students’ success in online and in remote learning. These models investigated which factors, ranging from demographic information to GPA, are significant predictors of both final grade and remote grade. I started the linear model selection process by testing a complex linear model, which had all the possible factors including interactions that can impact final grade or remote grade from the surveys. Once I knew which factors were significant from the complex model, I eliminated non-significant variables and created new models, comparing each model by their AIC values until I found the best-fit linear model for final grade and remote grade. AIC is a measurement of how well a linear model fits and the lower the AIC value the better fit the linear model has. After testing each linear model: GPA, students’ lecture section, remote grade, and exam average were significant to final grade. These models suggest that while remote grade is a significant predictor of final grade, no variable measured in this study is significant enough to impact remote grade. Differing from previous research, my results showed that there were no gaps in achievement amongst gender and underrepresented minority students. Although statistically no variable significantly impacted remote grade, there are trends amongst demographic variables and remote grade, suggesting some potential relationships that could be explored in future studies.

The reusing, recycling, and reduction of waste streams is seen as a viable sustainability strategy. One major waste stream is coffee grounds with about 11.5 million kilograms being generated per day in America of which 90% is landfilled. This waste stream can be repurposed into usable carbon-based materials to address issues of climate, pollution, or engineering applications. For my research, I am converting spent (used) coffee grounds into biochars, a type of carbon-based material, with different charring (burning) temperature to measure the removal of lead (Pb2+) from contaminated water. The charring temperature was changed in order to determine the optimal charring temperature for water treatment. This presentation will go into the maximum amount of lead the biochars can remove, how fast the biochars can remove the lead and the properties of biochars that allow for such removal. Further results, methodology, and modeling applications will be discussed in the presentation.