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

• URL: http://arxiv.org/abs/2602.20120v1
• Date: Mon, 23 Feb 2026 18:35:20 GMT
• Title: Enhancing Capstone Program Workflow: A Case Study on a Platform for Managing Academic-Industry Projects
• Authors: Rafael Corsi Ferrao, Luciano Pereira Soares,
• Abstract summary: We detail a web-based tool designed to streamline the management of Capstone projects at Insper.<n>We discuss the technological solutions and the challenges encountered throughout development and deployment.<n>We present usage data from recent years, offering insights that may prove valuable for institutions or teams developing similar tools in the future.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Deep Research: A Systematic Survey [118.82795024422722]
  Deep Research (DR) aims to combine the reasoning capabilities of large language models with external tools, such as search engines.<n>This survey presents a comprehensive and systematic overview of deep research systems.
  arXiv  Detail & Related papers  (2025-11-24T15:28:28Z)
• Empirical Analysis of Pull Requests for Google Summer of Code [0.0]
  The Google Summer of Code (GSoC) is a global initiative that matches students or new contributors with experienced mentors to work on open-source projects.<n>This study presents an empirical analysis of pull requests created by interns during the GSoC program.
  arXiv  Detail & Related papers  (2024-12-17T17:42:43Z)
• Project Archetypes: A Blessing and a Curse for AI Development [3.2157163136267943]
  The development of applications using machine learning and artificial intelligence provides a context in which existing archetypes might outdate and need to be questioned, adapted, or replaced. We analyzed 36 interviews from 21 projects between IBM Watson and client companies and identified four project archetypes members initially used to understand the projects. We then derive a new project archetype, cognitive computing project, from the interviews. It can inform future development projects based on AI-development platforms.
  arXiv  Detail & Related papers  (2024-08-08T08:52:19Z)
• Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions [50.545824691484796]
  We identify six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI)<n>We identify benefits, harms, and future research directions for the four main themes.<n>We discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• Enhancing Student Engagement in Large-Scale Capstone Courses: An Experience Report [2.7629502923028944]
  capstone courses offer students a valuable opportunity to gain hands-on experience in software development. coordinating a capstone course, especially for a large student cohort, can be a daunting task for academic staff. We outline the iterative development and refinement of our capstone course as it grew substantially in size over a span of six consecutive sessions.
  arXiv  Detail & Related papers  (2024-04-03T23:59:35Z)
• Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO [50.58083807719749]
  We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-agent systems. We will open-source our benchmark including the environment wrapper, baselines, a visualization tool, and selected policies for further research.
  arXiv  Detail & Related papers  (2023-08-30T07:16:11Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• A systematic literature review of capstone courses in software engineering [0.3536605202672354]
  capstone projects are a common way to provide students with hands-on experience and teach soft skills. This paper explores the characteristics of software engineering capstone courses presented in the literature.
  arXiv  Detail & Related papers  (2023-01-09T18:04:35Z)
• Artificial Intelligence for Scientific Research: Authentic Research Education Framework [6.772344064510275]
  We implement a program in which teams of students with complementary skills develop useful artificial intelligence (AI) solutions for researchers in natural sciences. Our approach also directly benefits scientists, who get an opportunity to evaluate the usefulness of machine learning for their specific needs.
  arXiv  Detail & Related papers  (2022-09-19T16:50:05Z)
• Towards Automated Process Planning and Mining [77.34726150561087]
  We present a research project in which researchers from the AI and BPM field work jointly together. We discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models.
  arXiv  Detail & Related papers  (2022-08-18T16:41:22Z)
• Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence through the Lens of Reproducibility [38.87910190291545]
  We explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam. The focal point of the course is a group project based on existing FACT-AI algorithms from top AI conferences, and writing a report about their experiences.
  arXiv  Detail & Related papers  (2021-11-01T10:58:35Z)
• Experience of Teaching Data Visualization using Project-based Learning [0.3437656066916039]
  We show which input was provided when necessary for students to achieve their goals. We discuss and compare the tools we found useful for students to accomplish their goals.
  arXiv  Detail & Related papers  (2021-10-21T16:47:34Z)
• Distributed Deep Learning in Open Collaborations [49.240611132653456]
  We propose a novel algorithmic framework designed specifically for collaborative training. We demonstrate the effectiveness of our approach for SwAV and ALBERT pretraining in realistic conditions and achieve performance comparable to traditional setups at a fraction of the cost.
  arXiv  Detail & Related papers  (2021-06-18T16:23:13Z)
• A Survey of Knowledge Tracing: Models, Variants, and Applications [70.69281873057619]
  Knowledge Tracing is one of the fundamental tasks for student behavioral data analysis. We present three types of fundamental KT models with distinct technical routes. We discuss potential directions for future research in this rapidly growing field.
  arXiv  Detail & Related papers  (2021-05-06T13:05:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.