Browsing by Subject "Topic modeling"

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Inclusive learning with assistant chatbot in massive open online courses : examining students’ perceptions, utilizations, and expectations
(2024-05) Han, Songhee; Liu, Min, Ed. D.; Min Kyung Lee; Grace MyHyun Kim; Xiaofen Hamilton
This study examines students’ learning experiences with an assistant chatbot in professional development MOOCs designed for journalists. Utilizing a mixed-methods approach, it focuses on the students’ learning experience’s sub-domains, such as social presence, teaching and cognitive presence, self-regulation, ease of use, and behavioral intention. Employing the Community of Inquiry (CoI) framework and the Technology Acceptance Model (TAM), the study first assesses the impact of demographics like age, gender, region, and native language on these learning experiences. The study revealed that age and gender had no significant influence on learning experiences, while geo-cultural regions showed variations, particularly in social presence and, teaching and cognitive presence. Socioeconomic regions demonstrated more notable differences, especially between lower-middle and high-income areas. However, the native language did not significantly influence learning experiences. Second, structural equation modeling (SEM) validated several hypothesized relationships, highlighting the positive impact of self-regulation on various other learning domains. Interestingly, teaching and cognitive presence did not significantly influence behavioral intention, nor was there a significant relationship between behavioral intention and use time. Age and socioeconomic region factors were identified as full moderators, while gender was a partial moderator from multigroup SEM results. Third, an extensive analysis of student interactions with the chatbot was conducted using various data sources. This analysis revealed eight key topics of chatbot interactions and showed predominantly neutral sentiments in the chatbot text logs. However, survey and interview data indicated a generally positive perception of the chatbot, especially noting its operational effectiveness and ease of use. Sentiments varied across socioeconomic regions, with more positive feedback from lower-income regions, while those from higher-income regions had higher expectations. The study also observed differences in navigational patterns between chatbot users and non-users in the course. Chatbot users exhibited more diverse navigations, indicating deeper engagement with course materials and a higher completion rate. In contrast, non-users followed a more structured progression, mainly relying on the predefined course path. Finally, the study highlighted students’ expectations for the chatbot, emphasizing the need for improvements in response accuracy, diversity, and additional capabilities like multi-language support. The findings emphasize the role of demographic variables in shaping student interactions with chatbots in MOOCs and suggest that modifying chatbot responses for inclusiveness could be key in meeting diverse student needs. The implications include that adhering to Universal Design for Learning principles, empowered by current advancements in AI-based chatbot technology, and informed by the CoI and TAM, could better address the diverse needs in MOOCs, especially in chatbot-enhanced learning environments.
News, nations, and power relations : a study of newsmaking and policymaking as transnational practices
(2017-08) Shahin, Syed Saif; Reese, Stephen D.; Bock, Mary A.; Johnson, Thomas J.; Sparrow, Bartholomew H.; Straubhaar, Joseph D.
In this dissertation, I examine the relationship between newsmaking and policymaking as interpretive practices that operate by making sense of the social world based on stocks of knowledge about the nation and its stature and role vis-à-vis other nations. To do so, I study the news coverage of “foreign aid” from four nations – the United States, Britain, India, and Pakistan – over a 15-year period (2001-15). I also examine foreign policymaking in the form of speeches delivered by the leaders of these nations over the same period at the United Nations General Assembly. While machine learning helps me conduct a broad exploration of my large-volume data, critical discourse analysis aided by natural language processing leads to a rich, contextually sensitive understanding of the data based on purposive samples. The analysis illustrates a mutually constitutive relationship between newsmaking and foreign policymaking in all four nations. Both the news media and the political elite in each of these nations draw upon similar conceptions of national identity, respectively. In addition, these conceptions are complementary and transnationally shared. Journalists and policymakers everywhere rely on the same discourses of neoliberalism as the natural economic order and unipolarity as the functional political order of international relations: featuring the United States as the global superpower that enforces a capitalist free trade regime, Britain as a secondary power that helps the U.S. maintain this regime, India as an emerging power that aspires to a secondary position of power similar to Britain’s, and Pakistan as a subordinate nation that values itself as an ally of the superpower. I thus show how nations become willing participants in their own subordination. I also argue that voluntary subordination takes place because newsmaking and policymaking reify nations as the basic building blocks of social reality – thus according ontological equivalence and agency to all the peoples of the world qua nations. Subordinate nations, in particular, value this illusory sense of equality and agency, but it paradoxically makes them complicit in maintaining a hegemonic international order that curtails their choices and leaves them open to exploitation.
What does money mean? : frames of wealth and economic identity in U.S. politics, 1980-2020
(2022-07-28) Park-Ozee, Dakota E.; Jarvis, Sharon E., 1969-; Hart, Roderick P; Stroud, Natalie J; Coe, Kevin
In this dissertation, I interrogate the ways political candidates’, elite journalists’, and everyday peoples’ discourses address the U.S. political hierarchy in terms of the role(s) of money in politics and of wealth and wealth-based identities. I am interested in if and how groups in the U.S. use or ignore differences in class and wealth to order who and what is important in our democratic republic. This dissertation focuses on that concern in six chapters. In the first chapter, I overview the historic and contemporary contexts of wealth in the United States, address the place of money in politics, and provide a theoretical framework for the project. In Chapter Two, I present the research design and methodological choices for the three cases that compose my analysis. In the subsequent chapters, I present my results. Chapter Three is an inductive, computer-assisted, quantitative analysis of wealth-based frames promoted by presidential candidates across 40 years of U.S. elections (1980-2020). Chapter Four uses a deductive, human-coded, quantitative content analysis to assess the frames propagated by print and television news organizations across the same period. Chapter Five uses open-ended survey responses from 2020 to inductively and qualitatively examine how the language of everyday individuals frames money and political power. In the closing chapter, I synthesize my findings and compare the wealth-based hierarchies crafted by presidential candidates, elite journalists, and everyday individuals in political and public life in the U.S. In the end, I argue there is a top-down effort to background class-based identities and flatten different socioeconomic experiences to the moniker of middle class, but there is also a ground-up rebuttal to challenge the overdetermining power of money in U.S. political life and use that power to create a moral, equitable democracy.