Master of Arts

thesisThe rise of digital media technologies has changed how we remember the past. This study examines the memorial functions of Web 2.0 and digital memories. I suggest that memory practices that use Web 2.0 technologies are not just extensions of older forms of human memory practice based on a dichotomy between technological and human memory practices in which one is seen as determining or changing the other; memory practice with/in materiality, specifically Web 2.0 memory practice, is a collective where heterogeneous realities are mingled in the same domain, and the intersection entails new meanings, capacities, and potentials of memories. Borrowing methodological insights from actor-network theory (ANT), I examine the human actors (users and administrator), Web 2.0 technologies (interface and database/server), and political factors (terms and policy) on the same ontological level to show how the mixture of social factors and technological elements becomes memories and/or memorial website. To illustrate this human-technical network of social media memory practice, I examine the online memorial site for the Korean ferry Sewol, Citizen Network Remembering The Sewol (www.sa416.org), an extensive online public documentation that commemorates the tragedy of the Korean ferry Sewol sinking. Through this study, I reveal the ways in which the various actors, including humans and nonhuman, function, and I show how each node of network intersects in the practices of memory production and the politics

Why belonging matters for college students’ academic engagement : antecedents and consequences of sense of classroom belonging

The purpose of this dissertation project was to explore the mechanisms through which sense of belonging affects academic engagement—behavioral, emotional, and cognitive—over time in varying college classroom situations. The study also examined the potential contribution of some course attributes to either facilitating or thwarting students’ sense of belonging on academic engagement. Moreover, this project sought to identify group differences between ethnic or racial minority students and non-minority students in terms of the effect of sense of belonging on academic engagement. Using structural equation modeling (SEM), the study tested a short-term longitudinal model that hypothesized the predictive relations among course attributes, sense of classroom belonging, and academic engagement at the beginning and toward the end of the semester. Participants were 295 undergraduate students from a public university in a southwestern state of the U.S., who were recruited via an online website dedicated to a subject pool that was offered through the department of Educational Psychology. Participants were asked to respond to online survey items in relation to one of their undergraduate courses, choosing from those courses they were taking in the current semester the course that had a subject pool requirement. Results from the study showed that participants’ sense of belonging in the classroom positively predicted academic engagement later in the semester, even after their motivation and course attributes were held constant. The results also indicated that mode of instruction and classroom goal structure had significant direct effects on participants’ perceived belongingness at the beginning of the semester and subsequently indirect effects on their course engagement over the semester. Finally, the multigroup SEM analysis revealed that the effect of sense of belonging was almost equally beneficial for ethnic or racial minority and non-minority groups, suggesting that sense of belonging generally matters for both groups. However, a more nuanced look at the data suggests that for the ethnic or racial minority group, it may be more important to nurture a supportive classroom climate and provide ample opportunities to connect with peers. Overall, results provide insights into the powerful impact of sense of belonging in college students’ engagement in the classroom.Educational Psycholog

Artificial intelligence approach for tomato detection and mass estimation in precision agriculture

Funding: This study was carried out with the support of “Research Program for Agricultural Science & Technology Development” (Project No: PJ013891012020), National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.Application of computer vision and robotics in agriculture requires sufficient knowledge and understanding of the physical properties of the object of interest. Yield monitoring is an example where these properties affect the quantified estimation of yield mass. In this study, we propose an image-processing and artificial intelligence-based system using multi-class detection with instance-wise segmentation of fruits in an image that can further estimate dimensions and mass. We analyze a tomato image dataset with mass and dimension values collected using a calibrated vision system and accurate measuring devices. After successful detection and instance-wise segmentation, we extract the real-world dimensions of the fruit. Our characterization results exhibited a significantly high correlation between dimensions and mass, indicating that artificial intelligence algorithms can effectively capture this complex physical relation to estimate the final mass. We also compare different artificial intelligence algorithms to show that the computed mass agrees well with the actual mass. Detection and segmentation results show an average mask intersection over union of 96.05%, mean average precision of 92.28%, detection accuracy of 99.02%, and precision of 99.7%. The mean absolute percentage error for mass estimation was 7.09 for 77 test samples using a bagged ensemble tree regressor. This approach could be applied to other computer vision and robotic applications such as sizing and packaging systems and automated harvesting or to other measuring instruments.Publisher PDFPeer reviewe

The Impact of Community Engagement on Undergraduate Social Responsibility Attitudes

The literature on student development cautions that social responsibility attitudes may stagnate or decline as students proceed through college. Given the importance of students’ future professional obligations to society, identifying ways to reverse this trend is crucial. In turn, an important aim of this study, situated at a large public university, is to evaluate the prospects of community engagement as a strategy to foster professional social responsibility development. The study uses longitudinal results from an instrument known as the Generalized Professional Responsibility Assessment (GPRA) to assess personal and professional social responsibility attitudes. The study’s sample includes 128 students who completed a survey both in 2017, when entering college, and in 2019, when near the midpoint of college. Findings indicate that social responsibility attitudes remain stagnant, and that students over that time period attach more importance to salary as compared to helping people when considering job priorities. Yet, results reveal that increased community engagement predicts growth in social responsibility attitudes, even when controlling for students’ pre-college social responsibility attitudes and demographic characteristics. Further, a novel contribution of this study is a focus on two sub-categories of community engagement: discipline-based and peer-based. Discipline-based community engagement appears to foster professional aspects of social responsibility, while community engagement experiences tied to peer interaction appear to exert greater impacts for non-White students. An observation derived from the study is that community engagement, particularly when it connects to a student’s discipline or draws on peer influences, could be an effective strategy to promote social responsibility development

Designing a Communication Bridge between Communities: Participatory Design for a Question-Answering AI Agent

How do we design an AI system that is intended to act as a communication bridge between two user communities with different mental models and vocabularies? Skillsync is an interactive environment that engages employers (companies) and training providers (colleges) in a sustained dialogue to help them achieve the goal of building a training proposal that successfully meets the needs of the employers and employees. We used a variation of participatory design to elicit requirements for developing AskJill, a question-answering agent that explains how Skillsync works and thus acts as a communication bridge between company and college users. Our study finds that participatory design was useful in guiding the requirements gathering and eliciting user questions for the development of AskJill. Our results also suggest that the two Skillsync user communities perceived glossary assistance as a key feature that AskJill needs to offer, and they would benefit from such a shared vocabulary

Inactivation of airborne viruses using vacuum ultraviolet photocatalysis for a flow-through indoor air purifier with short irradiation time

Many ultraviolet (UV)-based disinfection methods have been developed; however, these methods usually use the recirculating mode or need long irradiation periods due to its low photon energy. Vacuum UV (VUV) was recently found to be a promising light source, despite its ozone generation. In this study, we investigated photocatalysis reactions by VUV with short irradiation times (0.004-0.125 s) for simultaneously inactivating airborne MS2 viruses and degrading the generated ozone toward a flow-through air disinfection system with high flow-rates. We developed three effective shapes for the catalyst frame: 2mm and 5mm pleated, and spiral-type Pd-TiO2 catalysts. The 2mm pleated Pd-TiO2/VUV photocatalyst exhibited the highest activity for simultaneous MS2 inactivation and ozone degradation, and the catalytic activity was effective regardless of relative humidity. Considering the gas phase and catalyst surface effects, and the natural inactivation of VUV-irradiated but live MS2 viruses, the 2mm pleated Pd-TiO2/VUV and succeeding UV photocatalysis showed more than 90% in the overall inactivation efficiency with residual ozone of 35 ppb at an irradiation time of 0.009 s (flow-rate: 33l/min). In contrast, most UV-based purifiers take longer times for disinfection. This system has the potential for an alternative to conventional UV-based air purifiers.Copyrigh