Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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For my project I held a fashion show for my brand, Kenzie Kay, as part of Paris Fashion Week and released a Spring collection for sale.
The pandemic that hit in 2020 has boosted the growth of online learning that involves the booming of Massive Open Online Course (MOOC). To support this situation, it will be helpful to have tools that can help students in choosing between the different courses and can help instructors to understand what the students need. One of those tools is an online course ratings predictor. Using the predictor, online course instructors can learn the qualities that majority course takers deem as important, and thus they can adjust their lesson plans to fit those qualities. Meanwhile, students will be able to use it to help them in choosing the course to take by comparing the ratings. This research aims to find the best way to predict the rating of online courses using machine learning (ML). To create the ML model, different combinations of the length of the course, the number of materials it contains, the price of the course, the number of students taking the course, the course’s difficulty level, the usage of jargons or technical terms in the course description, the course’s instructors’ rating, the number of reviews the instructors got, and the number of classes the instructors have created on the same platform are used as the inputs. Meanwhile, the output of the model would be the average rating of a course. Data from 350 courses are used for this model, where 280 of them are used for training, 35 for testing, and the last 35 for validation. After trying out different machine learning models, wide neural networks model constantly gives the best training results while the medium tree model gives the best testing results. However, further research needs to be conducted as none of the results are not accurate, with 0.51 R-squared test result for the tree model.
This paper studies how the COVID-19 global pandemic influenced a new generation of investors into the stock market. The paper will take a look at the state of the financial markets and its participants before the pandemic, during the pandemic, and after the pandemic. It is important to note that as of this paper, the COVID-19 pandemic is far from being over—these conclusions and recommendations are based on the current trends within the financial market. The research concludes that the younger participants that joined the market were more risk tolerant, traded in large quantities with little money, and found many of their trading strategies on social media platforms. Further research also suggests that market sentiments were highly correlated with price differences in stocks and other securities. Along with a categorization for the new investors in the market, this paper will take a look at how the new participants have affected more traditional experienced investors that were in the stock market well before the pandemic, and their ability to give and take investment advice from the new generation. Key words: COVID-19 pandemic, risk average, pandemic investors, market sentiments
This paper studies how the COVID-19 global pandemic influenced a new generation of investors into the stock market. The paper will take a look at the state of the financial markets and its participants before the pandemic, during the pandemic, and after the pandemic. It is important to note that as of this paper, the COVID-19 pandemic is far from being over—these conclusions and recommendations are based on the current trends within the financial market. The research concludes that the younger participants that joined the market were more risk tolerant, traded in large quantities with little money, and found many of their trading strategies on social media platforms. Further research also suggests that market sentiments were highly correlated with price differences in stocks and other securities. Along with a categorization for the new investors in the market, this paper will take a look at how the new participants have affected more traditional experienced investors that were in the stock market well before the pandemic, and their ability to give and take investment advice from the new generation. Key words: COVID-19 pandemic, risk average, pandemic investors, market sentiments
