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Luciano Pereira Soares

Publications and source records attributed to Luciano Pereira Soares.

3 recordsLinked to original sources

Quantifying the Relationship Between Team Dysfunctions and Performance in Capstone Projects

This paper investigates the association between team dysfunctions and performance in the context of an engineering Capstone program. Capstone programs are considered fundamental in assessing the readiness of engineering and computer science students for professional practice, evaluating not only technical knowledge but also essential competencies such as teamwork, communication, design, and organization. Despite the recognized importance of team dynamics in such settings, their relationship with project performance remains insufficiently understood. Using survey data collected from student teams, we quantify team dynamics based on Lencioni's model of the five dysfunctions of a team, with all measures reverse-coded so that higher values indicate more positive team behaviors (i.e., lower dysfunction). The results indicate a weak-to-moderate relationship between team dynamics and performance. Each dysfunction score was computed as the sum of three corresponding survey items. All items were coded such that higher values indicate more positive team behaviors (i.e., lower dysfunction). Therefore, each score ranges from 3 to 9, where higher values reflect healthier team dynamics. This study analyzes empirical evidence on team effectiveness in project-based learning environments and offers insights for the design of Capstone programs. In particular, it highlights the importance of goal alignment and focus on deliverables while emphasizing that team dynamics should be considered alongside technical and contextual factors.

cs.SE↗

Enhancing Capstone Program Workflow: A Case Study on a Platform for Managing Academic-Industry Projects

Capstone projects are widely adopted by universities around the world as a culminating assessment in bachelor's degree programs. These projects typically involve student teams tackling complex, real-world problems proposed by external stakeholders, such as companies, NGOs, or research centers. Although they offer valuable hands-on experience, managing Capstone projects can be challenging due to their multiple stages and demands. The process typically begins by identifying students' interests, followed by sourcing and selecting potential projects from external organizations. After presenting these options to students, groups must be formed based on various criteria, including academic ranking, GPA, previous experience, and individual skill sets. In this paper, we detail a web-based tool designed to streamline the management of Capstone projects at Insper, with an emphasis on project sourcing and group formation. We also discuss the technological solutions and the challenges encountered throughout development and deployment. Furthermore, we present usage data from recent years, offering insights that may prove valuable for institutions or teams developing similar tools in the future.

cs.CY↗

Automated Formative Feedback for Short-form Writing: An LLM-Driven Approach and Adoption Analysis

This paper explores the development and adoption of AI-based formative feedback in the context of biweekly reports in an engineering Capstone program. Each student is required to write a short report detailing their individual accomplishments over the past two weeks, which is then assessed by their advising professor. An LLM-powered tool was developed to provide students with personalized feedback on their draft reports, guiding them toward improved completeness and quality. Usage data across two rounds revealed an initial barrier to adoption, with low engagement rates. However, students who engaged in the AI feedback system demonstrated the ability to use it effectively, leading to improvements in the completeness and quality of their reports. Furthermore, the tool's task-parsing capabilities provided a novel approach to identify potential student organizational tasks and deliverables. The findings suggest initial skepticism toward the tool with a limited adoption within the studied context, however, they also highlight the potential for AI-driven tools to provide students and professors valuable insights and formative support.

cs.CY↗