Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Filtering by
The topic of my thesis is Louis Armstrong and his influence on society and the Civil Rights Movement. The intent is to demonstrate how Louis Armstrong aided the Civil Rights Movement by using his music to promote social justice and racial equality. The focus will be on the context of African-Americans, their social status, and rights from the early 1900s to the mid-1900s. I will connect this to important events in that time such as the fight against Jim Crow Laws and how Louis Armstrong played a role in ending segregation. He accomplished this by pushing the movement forward through speeches, fund-raising events, and his innovation of jazz. Armstrong’s gift was a form of swing jazz that advanced improvisation and emotion of music.
He was criticized for playing to segregated audiences and was thought to keep offensive stereotypes alive. However, Louis Armstrong battled against these conspiracies by performing fund-raising events and through public political stances against the oppression of African-Americans. As an example, he was outspoken about his disapproval of government and the public for their treatment of the nine African-American students enrolled at Little Rock. This resulted in the first time the school would be unsegregated between whites and blacks. Louis Armstrong worked hard in the fight against segregation and used his mastery of jazz to advance the civil rights movement. Finally, I will make a proposal as to how society can learn from Louis Armstrong and how to inspire new innovative forms of positively influencing society to help the less fortunate.
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.