Letter From Barbara Sheese Wilson to Eleanor Snell, May, 1970

This letter from Barbara Sheese, Ursinus College Class of 1962, congratulates Eleanor Snell on the occasion of her retirement from Ursinus College.https://digitalcommons.ursinus.edu/snell_docs/1071/thumbnail.jp

A Solitary Solidarity: Conditions for Attunement in the Migration Crisis in Greece

In this dissertation I draw on ethnographic field work and qualitative interviews with activist volunteers in Greece in 2016 to explore the conditions for ethical and affective attunement in the face of crisis and complicity. I offer a thick description of the multiple injuries to one’s senses and sensemaking capacities and the contradictions, tensions, dilemmas that undermine the capacity for attunement, a term I use to refer to the overlapping abilities to feel, to be moved, and to locate oneself and to connect to others. I begin by developing a contextual analysis of the complex and contradictory machinery of migration management that includes both the policies and practices that make up the European border regime and the humanitarian aid industry, bringing together the literatures on border regimes and the European refugee crisis, volunteers in crisis, histories and the economy of humanitarian aid, the politics of bearing witness and geographies of responsibility. I go on outline my methods and methodological knots, describing how my questions evolved across three phases of iterative analysis. In my first results chapter, I explore the questions of why these volunteers show up, what they bring with them, what they encounter, why they stay, and how they navigate the chaotic/traumatic/spectacle landscape of the humanitarian border. In my second results chapter, I sketch the limit character of the humanitarian border and explore the affective, often unspeakable, dimensions of volunteers in crisis. I describe the traumatic ruptures to volunteers’ frames of reference and meaning as they confronted multiple, ongoing limit situations and discuss a number of isolating dynamics that produced/structured a solitary solidarity. I explore some of the ways in which these isolating dynamics structured ambivalent relationships with people living in camp and produced agonizing dilemmas as volunteers found themselves caught between enacting solidarity and embodying domination and regulation. I conclude by drawing on psychoanalytic theory and theories of relational ethics to discuss the protection of the capacity for attunement as an ethical obligation when intervening in crisis, especially in the face of complicity. I argue that this obligation demands an attention to sensemaking as a fundamentally relational/affective capacity, context-oriented understandings of trauma and grief that do not demand cognitive management or mastery and which allow for the unsayable and the unknowable, and intentional relational practices for the development of an affective skin

CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes

Computing educators face significant challenges in providing timely support to students, especially in large class settings. Large language models (LLMs) have emerged recently and show great promise for providing on-demand help at a large scale, but there are concerns that students may over-rely on the outputs produced by these models. In this paper, we introduce CodeHelp, a novel LLM-powered tool designed with guardrails to provide on-demand assistance to programming students without directly revealing solutions. We detail the design of the tool, which incorporates a number of useful features for instructors, and elaborate on the pipeline of prompting strategies we use to ensure generated outputs are suitable for students. To evaluate CodeHelp, we deployed it in a first-year computer and data science course with 52 students and collected student interactions over a 12-week period. We examine students' usage patterns and perceptions of the tool, and we report reflections from the course instructor and a series of recommendations for classroom use. Our findings suggest that CodeHelp is well-received by students who especially value its availability and help with resolving errors, and that for instructors it is easy to deploy and complements, rather than replaces, the support that they provide to students

Newspaper Construction of Homelessness in Western United States Cities

The paths to homelessness are complex and attributable to a combination of structural issues associated with poverty that can magnify personal vulnerabilities. However, as homelessness became more prominent in news media during the 1980s, media discourse increasingly focused on personal characteristics within the homeless population which cast people as personally responsible for their plight. Simultaneously, media explanations for homelessness that called attention to structural conditions that contribute to homelessness decreased during the decade. Scholars explain this shift by situating it within the social and political climate of the time. This study extends the line of research on homelessness in news media in order to understand how coverage of homelessness has changed between the 1980s and the 2010s. A quantitative content analysis examines newspaper articles in two cities in the western United States -- Portland, Oregon, and San Diego, California -- where homelessness is a prominent and enduring social and political issue. News articles are examined for changes between two time periods (1988-1990 and 2014-2016) in mentions of personal and structural factors as well as changes in the discussion of solutions for homelessness. Results show an increase over time in portrayals of structural factors that contribute to homelessness as well as an increase in talk about permanent housing solutions. However, mentions of personal problems and behaviors, such as mental illness and substance abuse, have also increased. This suggests that, while news discourse may be moving toward more nuanced portrayals that acknowledge societal factors, news media still tend to focus on characteristics of homelessness that can cast people as personally culpable

Efficient Classification of Student Help Requests in Programming Courses Using Large Language Models

The accurate classification of student help requests with respect to the type of help being sought can enable the tailoring of effective responses. Automatically classifying such requests is non-trivial, but large language models (LLMs) appear to offer an accessible, cost-effective solution. This study evaluates the performance of the GPT-3.5 and GPT-4 models for classifying help requests from students in an introductory programming class. In zero-shot trials, GPT-3.5 and GPT-4 exhibited comparable performance on most categories, while GPT-4 outperformed GPT-3.5 in classifying sub-categories for requests related to debugging. Fine-tuning the GPT-3.5 model improved its performance to such an extent that it approximated the accuracy and consistency across categories observed between two human raters. Overall, this study demonstrates the feasibility of using LLMs to enhance educational systems through the automated classification of student needs

Patterns of Student Help-Seeking When Using a Large Language Model-Powered Programming Assistant

Providing personalized assistance at scale is a long-standing challenge for computing educators, but a new generation of tools powered by large language models (LLMs) offers immense promise. Such tools can, in theory, provide on-demand help in large class settings and be configured with appropriate guardrails to prevent misuse and mitigate common concerns around learner over-reliance. However, the deployment of LLM-powered tools in authentic classroom settings is still rare, and very little is currently known about how students will use them in practice and what type of help they will seek. To address this, we examine students' use of an innovative LLM-powered tool that provides on-demand programming assistance without revealing solutions directly. We deployed the tool for 12 weeks in an introductory computer and data science course ($n = 52$), collecting more than 2,500 queries submitted by students throughout the term. We manually categorized all student queries based on the type of assistance sought, and we automatically analyzed several additional query characteristics. We found that most queries requested immediate help with programming assignments, whereas fewer requests asked for help on related concepts or for deepening conceptual understanding. Furthermore, students often provided minimal information to the tool, suggesting this is an area in which targeted instruction would be beneficial. We also found that students who achieved more success in the course tended to have used the tool more frequently overall. Lessons from this research can be leveraged by programming educators and institutions who plan to augment their teaching with emerging LLM-powered tools