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- All Subjects: Machine Learning
- All Subjects: Video Games
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
- Status: Published
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University Devils is a Founders Lab Thesis group looking to find a way for post-secondary institutions to increase the number of and diversity of incoming applications through the utilization of gaming and gaming approaches in the recruitment process while staying low-cost. This propelling question guided the group through their work. The team’s work primarily focused on recruitment efforts at Arizona State University, but the concept can be modified and applied at other post-secondary institutions. The initial research showed that Arizona State University’s recruitment focused on visiting the high schools of prospective students and providing campus tours to interested students. A proposed alternative solution to aid in recruitment efforts through the utilization of gaming was to create an online multiplayer game that prospective students could play from their own homes. The basic premise of the game is that one player is selected to be “the Professor” while the other players are part of “the Students.” To complete the game, the Students must complete a set of tasks while the Professor applies various obstacles to prevent the Students from winning. When a Student completes their objectives, they win and the game ends. The game was created using Unity. The group has completed a proof-of-concept of the proposed game and worked to advertise and market the game to students via social media. The team’s efforts have gained traction, and the group continues to work to gain traction and bring the idea to more prospective students.
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In this paper, I introduce the fake news problem and detail how it has been exacerbated<br/>through social media. I explore current practices for fake news detection using natural language<br/>processing and current benchmarks in ranking the efficacy of various language models. Using a<br/>Twitter-specific benchmark, I attempt to reproduce the scores of six language models<br/>demonstrating their effectiveness in seven tweet classification tasks. I explain the successes and<br/>challenges in reproducing these results and provide analysis for the future implications of fake<br/>news research.