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Description
Hackathons are 24-36 hour events where participants are encouraged to learn, collaborate, and build technological inventions with leaders, companies, and peers in the tech community. Hackathons have been sweeping the nation in the recent years especially at the collegiate level; however, there is no substantial research or documentation of the actual effects of hackathons especially at the collegiate level. This makes justifying the usage of valuable time and resources to host hackathons difficult for tech companies and academic institutions. This thesis specifically examines the effects of collegiate hackathons through running a collegiate hackathon known as Desert Hacks at Arizona State University (ASU). The participants of Desert Hacks were surveyed at the start and at the end of the event to analyze the effects. The results of the survey implicate that participants have grown in base computer programming skills, inclusion in the tech community, overall confidence, and motivation for the technological field. Through these results, this study can be used to help justify the necessity of collegiate hackathons and events similar.
ContributorsLe, Peter Thuan (Author) / Atkinson, Robert (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
In today's world, technology plays a large role in everyone's life. However, there is a short supply of professionals to fill the roles in the computing field. When examining closer, it is clear that one group has a smaller representation: women. This can be contributed to many factors early in the women's lives and academic careers. In hopes of increasing the number of women computing professionals, this thesis aimed to understand the problem of a lack of women in technology and studied how hackathons could be a possible solution. The research followed Desert Hacks as it examines the typical participants as well as the hackathons effects on women's morale in technology. Two important questions during the investigation were what kind of women are attending hackathons and how do women feel about the technology industry after a hackathon? The results suggested that hackathon had an overall positive effect on women's motivation in the computing field. Additionally, most research participants believed that everyone has the potential to do well in the field and that gender inclusion is important for the industry. This ideology can foster a healthy environment for women to become more motivated in computing. Through these results, hackathons can be seen as another mean to help motivate women in the field and open up the possibility of future studies of women and hackathons.
ContributorsVo, Thong Bach (Author) / Atkinson, Robert (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Currently, recommender systems are used extensively to find the right audience with the "right" content over various platforms. Recommendations generated by these systems aim to offer relevant items to users. Different approaches have been suggested to solve this problem mainly by using the rating history of the user or by identifying the preferences of similar users. Most of the existing recommendation systems are formulated in an identical fashion, where a model is trained to capture the underlying preferences of users over different kinds of items. Once it is deployed, the model suggests personalized recommendations precisely, and it is assumed that the preferences of users are perfectly reflected by the historical data. However, such user data might be limited in practice, and the characteristics of users may constantly evolve during their intensive interaction between recommendation systems.
Moreover, most of these recommender systems suffer from the cold-start problems where insufficient data for new users or products results in reduced overall recommendation output. In the current study, we have built a recommender system to recommend movies to users. Biclustering algorithm is used to cluster the users and movies simultaneously at the beginning to generate explainable recommendations, and these biclusters are used to form a gridworld where Q-Learning is used to learn the policy to traverse through the grid. The reward function uses the Jaccard Index, which is a measure of common users between two biclusters. Demographic details of new users are used to generate recommendations that solve the cold-start problem too.
Lastly, the implemented algorithm is examined with a real-world dataset against the widely used recommendation algorithm and the performance for the cold-start cases.
Moreover, most of these recommender systems suffer from the cold-start problems where insufficient data for new users or products results in reduced overall recommendation output. In the current study, we have built a recommender system to recommend movies to users. Biclustering algorithm is used to cluster the users and movies simultaneously at the beginning to generate explainable recommendations, and these biclusters are used to form a gridworld where Q-Learning is used to learn the policy to traverse through the grid. The reward function uses the Jaccard Index, which is a measure of common users between two biclusters. Demographic details of new users are used to generate recommendations that solve the cold-start problem too.
Lastly, the implemented algorithm is examined with a real-world dataset against the widely used recommendation algorithm and the performance for the cold-start cases.
ContributorsSargar, Rushikesh Bapu (Author) / Atkinson, Robert K (Thesis advisor) / Chen, Yinong (Thesis advisor) / Chavez-Echeagaray, Maria Elena (Committee member) / Arizona State University (Publisher)
Created2020