Understanding Shifting Dynamics of Power in State Governments through Social Networks

We use social network analysis to better understand historic data on the administration of local governments. Despite advances in e-government applications, the public sector lags behind in analytics because information is locked in legacy data formats. Can e-government researchers bridge the gap between legacy data and analytics? We argue that computational analytic methods can explain patterns that have gone unquestioned in previous research on government. We consider how state government authority can be explained using a network perspective. We investigate methodological challenges in building a weighted network to confirm existing measures for calculating the power of the state governor. This project reports on the initial step in a broader study to cover all 50 states across multiple years and agencies. We explain where the power shifted across states and time. Computational analysis of existing government data matches findings from previous studies as well as adding additional explanatory power

Measuring and Unpacking Affective Polarization on Twitter: The Role of Party and Gender in the 2018 Senate Races

This study examines how the Twittersphere talked about candidates running for the U.S senate in the 2018 congressional elections. We classify Twitter users as Liberal or Conservative to better understand how the two groups use social media during a major national political election. Using tweet sentiment, we assess how the Twittersphere felt about in-group party versus out-group party candidates. When we further break these findings down based on the candidates’ gender, we find that male senatorial candidates were talked about more positively than female candidates. We also find that Conservatives talked more positively about female Republican candidates than they did about Republican male candidates. Female candidates of the out-group party were talked about the least favorably of all candidates. Conservative tweeters exhibit the most positive level of in-group party sentiment and the most negative level of out-group party sentiment. We therefore attribute the most intense affective polarization to this ideological group

From Engagement to Empowerment: Project-Based Learning in Python Coding Courses

Project-based learning (PBL) engages students deeply with course concepts and empowers them to drive their own learning through the development of solutions to real-world challenges. By taking ownership of and completing a project that they designed, students develop and demonstrate creativity, critical thinking, and collaboration skills. This paper describes two different software development projects, designed with a PBL approach, in Python coding courses at two business universities in the United States, in which students queried real-world data to answer their own questions and interpret the results. The authors contend that projects based on a PBL approach motivate students for selfexploration and allow for the measure of student learning. The authors present their respective projects, share examples of student work, and offer suggestions and lessons learned from implementing PBL assignments in their classrooms. Finally, the authors reflect, through sharing student comments, on how key aspects of PBL are manifest in this project and discuss challenges in offering and managing PBL assignments. With Python\u27s popularity on the rise, these two class examples serve as a model for how instructors can incorporate autonomy in PBL assignments, offering a valuable learning opportunity for students to create software applications that meaningfully demonstrate their coding skills

Blockchain Technology and The Current Discussion on Fraud

Blockchain has received increased attention from both the academic and practitioner worlds. Numerous papers have been written on how blockchain works and its potential applications. However, few studies have focused on fraudulent activities on blockchain. The purpose of this study is to understand common issues and scams related to blockchain. A literature review was conducted to identify top security issues on blockchain. In addition, we collected tweets on blockchain fraud discussion from November 6, 2018 to December 31, 2008. The results of tweets analysis show that the most frequently mentioned words in tweets include scams, crypto/cryptocurrency, ICO, Bitcoin, Ethereum,combat/fight, Asia, Japan, and Germany. The top mentioned sentiment words include scam, combat, guilty, prevent,solution, tired, and fake. In addition, a sentiment analysis shows that the majority of the tweets (69%) on the discussion on blockchain fraud are negative. The findings also shows the majority of top influencers of the topic are the companies that have developed blockchain-based platforms/applications

Teaching Applications and Implications of Blockchain via Project-Based Learning: A Case Study

This paper presents student projects analyzing or using blockchain technologies, created by students enrolled in courses dedicated to teaching blockchain, at two different universities during the 2018-2019 academic year. Students explored perceptions related to storing private healthcare information on a blockchain, managing the security of Internet of Things devices, maintaining public governmental records, and creating smart contracts. The course designs, which were centered around project-based learning, include self-regulated learning and peer feedback as ways to improve student learning. Students either wrote a research paper or worked in teams on a programming project to build and deploy a blockchain-based application using Solidity, a programming language for writing smart contracts on various blockchain platforms. For select student papers, this case study describes research methods and outcomes and how students worked together or made use of peer feedback to improve upon drafts of research questions and abstracts. For a development project in Solidity, this study presents the issues at hand along with interview results that guided the implementation. Teams shared lessons learned with other teams through a weekly status report to the whole class. While available support for the Solidity teams was not ideal, students learned to use available online resources for creating and testing smart contracts. Our findings suggest that a project-based learning approach is an effective way for students to expand and develop their knowledge of emerging technologies, like blockchain, and apply it in a variety of industrie

Music March Madness: Predicting the Winner of Locura de Marzo

Each Spring, thousands of middle and high school students enrolled in Spanish classes vote for their favorite songs in the annual Locura De Marzo competition. This alternative March Madness competition gives us an opportunity to build and test models to predict which songs will win which furthers the Hit Song Science literature. Using decision trees and support vector machine (SVM) models we find similarities with the challenge of predicting the popular NCAA Basketball bracket including the importance of seed and the difficulty in predicting a “perfect” bracke