Browsing by Author "Neyem, Hugo Andrés"

Now showing 1 - 11 of 11
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    Communication patterns to support mobile collaboration
    (2009) Neyem, Hugo Andrés; Ochoa, S. F.; Pino, J. A.; Carriço, Luís; Baloian, Nelson; Fonseca, Benjamim
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    DGT-AR: Visualizing Code Dependencies in AR
    (2023) Freire-Pozo, Dussan; Céspedes Arancibia, Kevin; Merino Del Campo, Leonel Alejandro; Fernández, Blanco Alison; Neyem, Hugo Andrés; Sandoval Alcocer, Juan Pablo
    Analyzing source code dependencies between components within a program is an essential activity in software development. While various software visualization tools have been proposed to aid in this activity, most are limited to desktop applications. As a result, the potential impact of augmented reality (AR) on improving dependency analysis remains largely unexplored. In this paper, we present DGT-AR, a node-link visualization tool for code dependencies in immersive augmented reality. DG T-AR extends the physical screen space of IDEs to the infinite virtual space. That is, developers neither have to sacrifice screen space nor leave the IDE and use third-party applications. We present the preliminary results of a pilot user study along with four key lessons learned. Additionally, we have made DGT-AR publicly available.
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    Enhancing Feedback Uptake and Self-Regulated Learning in Procedural Skills Training
    (2024) Villagrán Gutiérrez, Ignacio Andrés; Hernández Román, Rocío Belén; Schuit Condell, Gregory Kees; Neyem, Hugo Andrés; Fuentes Cimma, Javiera Carolina; Larrondo Vergara, María Loreto; Margozzini Delorenzo, Elisa; Hurtado Bunster, María Teresa; Iriarte Vásquez, Zoe; Miranda Mendoza, Constanza Sofía; Varas Cohen, Julián Emanuel; Hilliger Carrasco, Isabel
    Remote technology has been widely incorporated into health professions education. For procedural skills training, effective feedback and reflection processes are required. Consequently, supporting a self-regulated learning (SRL) approach with learning analytics dashboards (LADs) has proven beneficial in online environments. Despite the potential of LADs, understanding their design to enhance SRL and provide useful feedback remains a significant challenge. Focusing on LAD design, implementation, and evaluation, the study followed a mixed-methods two-phase design-based research approach. The study used a triangulation methodology of qualitative interviews and SRL and sensemaking questionnaires to comprehensively understand the LAD’s effectiveness and student SRL and feedback uptake strategies during remote procedural skills training. Initial findings revealed the value students placed on performance visualization and peer comparison despite some challenges in LAD design and usability. The study also identified the prominent adoption of SRL strategies such as help-seeking, elaboration, and strategic planning. Sensemaking results showed the value of personalized performance metrics and planning resources in the LAD and recommendations to improve reflection and feedback uptake. Subsequent findings suggested that SRL levels significantly predicted the levels of sensemaking. The students valued the LAD as a tool for supporting feedback uptake and strategic planning, demonstrating the potential for enhancing procedural skills learning.
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    Exploring the Impact of Generative AI for StandUp Report Recommendations in Software Capstone Project Development
    (2024) Neyem, Hugo Andrés; Sandoval Alcocer, Juan Pablo; Mendoza Rocha, Marcelo Gabriel; Centellas-Claro, Leonardo; González, Luis A.; Paredes Robles, Carlos Daniel
    StandUp Reports play an important role in capstone software engineering courses, facilitating progress tracking, obstacle identification, and team collaboration. However, despite their significance, students often grapple with the challenge of creating StandUp Reports that are clear, concise, and actionable. This paper investigates the impact of the use of generative AI in producing StandUp report recommendations, aiming to assist students in enhancing the quality and effectiveness of their reports. In a semester-long capstone course, 179 students participated in 16 real-world software development projects. They submitted weekly StandUp Reports with the assistance of an AI-powered Slack, which analyzed their initial reports and provided suggestions for enhancing them using both GPT-3.5 and the early access GPT-4 API. After each submitted report, students voluntarily answered a survey about usability and suggestion preference. Furthermore, we conducted a linguistic analysis of the recommendations made by the algorithms to gauge reading ease and comprehension complexity. Our findings indicate that the AI-based recommendation system helped students improve the overall quality of their StandUp Reports throughout the semester. Students expressed a high level of satisfaction with the tool and exhibited a strong willingness to continue using it in the future. The survey reveals that students perceived a slight improvement when using GPT-4 compared to GPT-3.5. Finally, a computational linguistic analysis performed on the recommendations demonstrates that both algorithms significantly improve the alignment between the generated texts and the students' educational level, thereby improving the quality of the original texts.
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    Inovações no treinamento cirúrgico: explorando o papel da inteligência artificial e dos grandes modelos de linguagem (LLM)
    (2023) Varas Cohen, Julián Emanuel; Valencia Coronel, Brandon; Villagrán Gutiérrez, Ignacio Andrés; Escalona Vivas, Gabriel Enrique; Hernández Román, Rocío Belén; Schuit Condell, Gregory Kees; Duran Espinoza, Valentina Alexandra; Lagos Villaseca, Antonia Elisa; Jarry Trujillo, Cristián Ignacio; Neyem, Hugo Andrés; Achurra Tirado, Pablo Andrés
    O cenário do treinamento cirúrgico está evoluindo rapidamente com o surgimento da inteligência artificial (IA) e sua integração na educação e simulação. Este artigo explora as aplicações e benefícios potenciais do treinamento cirúrgico assistido por IA, em particular o uso de modelos de linguagem avançados (MLAs), para aprimorar a comunicação, personalizar o feedback e promover o desenvolvimento de habilidades. Discutimos os avanços no treinamento baseado em simulação, ferramentas de avaliação impulsionadas por IA, sistemas de avaliação baseados em vídeo, plataformas de realidade virtual (RV) e realidade aumentada (RA), e o papel potencial dos MLAs na transcrição, tradução e resumo do feedback. Apesar das oportunidades promissoras apresentadas pela integração da IA, vários desafios devem ser abordados, incluindo precisão e confiabilidade, preocupações éticas e de privacidade, viés nos modelos de IA, integração com os sistemas de treinamento existentes, e treinamento e adoção de ferramentas assistidas por IA. Ao abordar proativamente esses desafios e aproveitar o potencial da IA, o futuro do treinamento cirúrgico pode ser remodelado para proporcionar uma experiência de aprendizado mais abrangente, segura e eficaz para os aprendizes, resultando em melhores resultados para os pacientes.
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    On the use of statistical machine translation for suggesting variable names for decompiled code: The Pharo case
    (2024) Sandoval Alcocer, Juan Pablo; Camacho-Jaimes, Harold; Galindo-Gutierrez, Geraldine; Neyem, Hugo Andrés; Bergel, Alexandre; Ducassee, Stéphane
    Adequately selecting variable names is a difficult activity for practitioners. In 2018, Jaffe et al. proposed the use of statistical machine translation (SMT) to suggest descriptive variable names for decompiled code. A large corpus of decompiled C code was used to train the SMT model. Our paper presents the results of a partial replication of Jaffe’s experiment. We apply the same technique and methodology to a dataset made of code written in the Pharo programming language. We selected Pharo since its syntax is simple – it fits on half of a postcard – and because the optimizations performed by the compiler are limited to method scope. Our results indicate that SMT may recover between 8.9% and 69.88% of the variable names depending on the training set. Our replication concludes that: (i) the accuracy depends on the code similarity between the training and testing sets; (ii) the simplicity of the Pharo syntax and the satisfactory decompiled code alignment have a positive impact on predicting variable names; and (iii) a relatively small code corpus is sufficient to train the SMT model, which shows the applicability of the approach to less popular programming languages. Additionally, to assess SMT’s potential in improving original variable names, ten Pharo developers reviewed 400 SMT name suggestions, with four reviews per variable. Only 15 suggestions (3.75%) were unanimously viewed as improvements, while 45 (11.25%) were perceived as improvements by at least two reviewers, highlighting SMT’s limitations in providing suitable alternatives.
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    Plataforma de Software Educativa Gamificada: experiencia con estudiantes de anatomía de la Universidad de La Frontera
    (2022) Stambuk-Castellano, Mónica; Contreras-Mc kay, Ignacio; Neyem, Hugo Andrés; Inzunza Hernández, Oscar Alejandro; Ottone, Nicolas E.; Del Sol, Mariano
    La tecnología ha abierto la posibilidad de mejorar los entornos de aprendizaje. Sin embargo, en el ámbito de la educación médica, las herramientas que son utilizadas no entregan evidencias claras sobre si los estudiantes realmente están aprendiendo. Específicamente, en la enseñanza de la anatomía han surgido múltiples aplicaciones para satisfacer la necesidad de acceder a material cadavérico, no obstante, éstas carecen de información enriquecida sobre el rendimiento que alcanzan los estudiantes y del cómo adaptar los aprendizajes según sus necesidades educativas. Así, una de las estrategias que actualmente tiene presencia en este ámbito es la gamificación. Este estudio implementa y utiliza una plataforma de software educativa gamificada basada en sistemas de recomendación y asistentes virtuales, capaz de entregar retroalimentación y estrategias para apoyar la apropiación de conocimiento de anatomía de los estudiantes de la carrera de medicina de la Universidad de La Frontera (UFRO), de la ciudad de Temuco, Chile. Cuarenta y cinco estudiantes participaron del estudio. Éste consistió en la utilización de diversos componentes gamificados con técnicas de inteligencia artificial. Los principales hallazgos de esta experiencia permitieron concluir que la utilización de componentes gamificados para el aprendizaje de la anatomía son un recurso que permite apoyar el aprendizaje de los estudiantes.
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    Registro Electrónico Nacional de Prescripción de Estupefacientes y Psicotrópicos: una mirada a posibles ventajas y dificultades de implementación
    (Pontificia Universidad Católica de Chile, 2024) Altermatt Couratier, Fernando René; Leon Stehr, Paula Jacinta; Goic Boroevic, Carolina; Leniz Martelli, Javiera; Ramos Vergara, Paulina Cecilia; Neyem, Hugo Andrés; Verges Gómez, Álvaro Javier; Aranguiz Villagran, Matías Andrés; Centro de Políticas Públicas UC; Pontificia Universidad Católica de Chile. Escuela de Medicina; Pontificia Universidad Católica de Chile. Facultad de Ingeniería; Pontificia Universidad Católica de Chile. Facultad de Derecho; Universidad de los Andes. Escuela de Psicología
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    Understanding presence awareness information needs among engineering students
    (IEEE, 2012) Herskovic Maida, Valeria Paz; Neyem, Hugo Andrés; Ochoa, Sergio F.; Pino, José A.; Antunes, Pedro
    The flexibility and changing nature of loosely coupled work makes presence awareness crucial to promote interactions among collaborators. Undergraduate students, in their efforts to accomplish coursework-related tasks, must deal with having several available channels to interact with others, accessing and sharing educational material, and the need to optimize their time. Most of them work in a loosely coupled way as the main strategy to reduce the effort spent in the educational process. Presence awareness may help them achieve interactions among potential collaborators in this scenario. This paper aims to identify the most suitable presence awareness information to promote on-demand interactions among college students. A study was conducted for this purpose, involving undergraduate engineering students from two universities in Chile. This article also presents a classification of presence awareness mechanisms for loosely-coupled mobile group work.
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    Understanding student interactions in capstone courses to improve learning experiences
    (2017) Neyem, Hugo Andrés; Diaz Mosquera, Juan Diego; Muñoz Gama, Jorge; Navon Cohen, Jaime
    Project-based courses can provide valuable learning experiences for computing majors as well as for faculty and community partners. However, proper coordination between students, stakeholders and the academic team is very difficult to achieve. We present an integral study consisting of a twofold approach. First, we propose a proven capstone course framework implementation in conjunction with an educational software tool to support and ensure proper fulfillment of most academic and engineering needs. Second, we propose an approach for mining process data from the information generated by this tool as a way of understanding these courses and improving software engineering education. Moreover, we propose visualizations, metrics and algorithms using Process Mining to provide an insight into practices and procedures followed during various phases of a software development life cycle. We mine the event logs produced by the educational software tool and derive aspects such as cooperative behaviors in a team, component and student entropy, process compliance and verification. The proposed visualizations and metrics (learning analytics) provide a multi-faceted view to the academic team serving as a tool for feedback on development process and quality by students
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    Vecino Virtual
    Osorio Schmied, David Andrés; Foxley Tapia, Susana; Whittle Navarro, Johanna Rose; Fernández Valdés, Gregorio Patricio; Neyem, Hugo Andrés