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

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

Associação entre fatores individuais e contextuais e o desempenho cognitivo em pré-escolares com necessidades básicas insatisfeitas

En el marco de un proyecto de intervención, orientado a optimizar el desempeño cognitivo a través de actividades de juego para madres y sus hijos, este estudio presenta los resultados de un análisis de asociación entre factores (a) individuales (i.e. cortisol; actividad electroencefalográfica; lenguaje; y salud), y (b) contextuales (i.e. características del hogar; salud materna; y lenguaje materno), con la eficiencia en la solución de tareas con demandas cognitivas, en una muestra de 46 niños de 5 años de edad, sin historia del trastorno del desarrollo, y pertenecientes a hogares con NBI. Luego de aplicar análisis no paramétricos de tendencias entre grupos, los resultados indicaron a los siguientes como los factores de mayor asociación con el desempeño cognitivo: (a) conectividad y potencia neurales; y (b) lenguaje materno. El abordaje implementado contribuye con una mejora en la comprensión de las asociaciones entre factores individuales y contextuales del desempeño cognitivo, al considerar diferentes niveles de organización involucrados en su desarrollo.In the context of an experimental intervention aimed at optimizing cognitive development through play activities for mothers and their children, this study presents the results of an association analysis between (a) individual (i.e. cortisol, electroencephalographic activity, language, and health conditions), and (b) contextual factors (i.e. home characteristics, maternal health, and mother language) with the efficiency in task solution with cognitive demands, in a sample of 46 5-years-old children, with no history of developmental disorder, and from UBN homes. After applying non-parametric trend analyses between groups, the results indicated the following as the factors of greatest association with cognitive performance: (a) neural connectivity and power; and (b) mother language. The implemented approach contributes to the understanding of the associations between individual and contextual factors of cognitive performance, considering different levels of organization involved in its development.No contexto de uma intervenção experimental objetivando otimizar o desenvolvimento cognitivo através de atividades lúdicas para mães e seus filhos, este estudo apresenta os resultados de uma análise de associação entre (a) atividade individual (cortisol, atividade eletroencefalográfica, linguagem e condições de saúde) e (b) fatores contextuais (características domiciliares, saúde materna e língua materna), com a eficiência em solução de tarefas com demandas cognitivas, em uma amostra de 46 crianças de 5 anos de idade, sem história de transtorno de desenvolvimento e de domicílios com necessidades básicas insatisfeitas. Após a aplicação de análises de tendências não-paramétricas entre os grupos, os resultados indicaram os seguintes fatores de maior associação com o desempenho cognitivo: (a) conectividade neural e poder; E (b) a língua materna. A abordagem implementada contribui para a compreensão das associações entre fatores individuais e contextuais de desempenho cognitivo, considerando diferentes níveis de organização envolvidos em seu desenvolvimento.Fil: Prats, Lucía María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Segretin, María Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Fracchia, Carolina Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Kamienkowski, Juan Esteban. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Laboratorio de Inteligencia Artificial Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pietto, Marcos Luis. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Laboratorio de Inteligencia Artificial Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Hermida, Maria Julia. Universidad Torcuato di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Giovannetti, Federico. Centro de Educación Médica e Investigaciones Clínicas “Norberto Quirno”; ArgentinaFil: Mancini, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; ArgentinaFil: Gravano, Agustin. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Laboratorio de Inteligencia Artificial Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sheese, Brad. Illinois Wesleyan University; Estados UnidosFil: Lipina, Sebastián Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; Argentin

The Impact of Dexamethasone on Behavioral Development in Larval Zebrafish (\u3cem\u3eDanio Rerio\u3c/em\u3e)

Stress can have damaging effects on a developing organism. Cortisol, a glucocorticoid that is a critical component of the stress response, can readily permeate across the placenta barrier and may have long-lasting, deleterious effects on the stress response of the developing organism. The synthetic homologue of cortisol is dexamethasone (dex), a corticosteroid used as an anti-inflammatory drug in humans to treat various autoimmune diseases and cancer. Dex is also often given to preterm infants to accelerate fetal lung development prior to delivery. Research with animal models has linked prenatal dexamethasone exposure to increased fear reactivity, decreased locomotor activity, and elevated cortisol levels in response to stress, in addition to brain cell loss and neurodevelopmental disability. The present study uses larval Zebrafish (Danio rerio) to examine the effects of dex exposure on anxiety-related behavior during a window critical for Hypothalamic-Pituitary-Interrenal axis development. By varying exposure to dex (dex vs. control) and timing of exposure (0 to 12 hours or 12 to 24 hours post fertilization), and examining the effect on motor behaviors in 5 day old zebrafish larvae, this study will advance our understanding of the mechanisms by which stress may cause long-term changes in stress reactivity