Agile Minds, Innovative Solutions, and Industry-Academia Collaboration: Lean R&D Meets Problem-Based Learning in Software Engineering Education

• URL: http://arxiv.org/abs/2407.15982v1
• Date: Mon, 22 Jul 2024 18:47:14 GMT
• Title: Agile Minds, Innovative Solutions, and Industry-Academia Collaboration: Lean R&D Meets Problem-Based Learning in Software Engineering Education
• Authors: Lucas Romao, Marcos Kalinowski, Clarissa Barbosa, Allysson Allex Araújo, Simone D. J. Barbosa, Helio Lopes,
• Abstract summary: This paper aims to extend Lean R&D with skill principles, emphasizing business and software development synergy. The educational program engaged 40 part-time students receiving lectures and mentoring while working on real problems. Students reported increased knowledge proficiency and perceived working on real problems as contributing the most to their learning.
• Score: 1.4454625330080995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: [Context] Software Engineering (SE) education constantly seeks to bridge the gap between academic knowledge and industry demands, with active learning methods like Problem-Based Learning (PBL) gaining prominence. Despite these efforts, recent graduates struggle to align skills with industry needs. Recognizing the relevance of Industry-Academia Collaboration (IAC), Lean R&D has emerged as a successful agile-based research and development approach, emphasizing business and software development synergy. [Goal] This paper aims to extend Lean R&D with PBL principles, evaluating its application in an educational program designed by ExACTa PUC- Rio for Americanas S.A., a large Brazilian retail company. [Method] The educational program engaged 40 part-time students receiving lectures and mentoring while working on real problems, coordinators and mentors, and company stakeholders in industry projects. Empirical evaluation, through a case study approach, utilized structured questionnaires based on the Technology Acceptance Model (TAM). [Results] Stakeholders were satisfied with Lean R&D PBL for problem-solving. Students reported increased knowledge proficiency and perceived working on real problems as contributing the most to their learning. [Conclusion] This research contributes to academia by sharing Lean R&D PBL as an educational IAC approach. For industry, we discuss the implementation of this proposal in an IAC program that promotes workforce skill development and innovative solutions.

Related papers

• Using Case Studies to Teach Responsible AI to Industry Practitioners [8.152080071643685]
  We propose a novel stakeholder-first educational approach that uses interactive case studies to achieve organizational and practitioner -level engagement and advance learning of Responsible AI (RAI) Our assessment results indicate that participants found the workshops engaging and reported a positive shift in understanding and motivation to apply RAI to their work.
  arXiv  Detail & Related papers  (2024-07-19T22:06:06Z)
• The global landscape of academic guidelines for generative AI and Large Language Models [8.420666056013685]
  The integration of Generative Artificial Intelligence (GAI) and Large Language Models (LLMs) in academia has spurred a global discourse on their potential pedagogical benefits and ethical considerations. Positive reactions highlight some potential, such as collaborative creativity, increased access to education, and empowerment of trainers and trainees. However, negative reactions raise concerns about ethical complexities, balancing innovation and academic integrity, unequal access, and misinformation risks.
  arXiv  Detail & Related papers  (2024-05-26T15:28:24Z)
• Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
  We shed light on the intersectional studies of generative AI and adaptive learning. We argue that this union will contribute significantly to the development of the next-stage learning format in education.
  arXiv  Detail & Related papers  (2024-02-02T23:54:51Z)
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice [64.29355073494125]
  This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation. We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
  arXiv  Detail & Related papers  (2023-10-02T05:30:42Z)
• Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community [180.28262433004113]
  Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference. LLMs and humans form a new technical paradigm that is more powerful for information seeking. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023.
  arXiv  Detail & Related papers  (2023-07-19T05:23:43Z)
• The Equitable AI Research Roundtable (EARR): Towards Community-Based Decision Making in Responsible AI Development [4.1986677342209004]
  The paper reports on our initial evaluation of The Equitable AI Research Roundtable. EARR was created in collaboration among a large tech firm, nonprofits, NGO research institutions, and universities. We outline three principles in practice of how EARR has operated thus far that are especially relevant to the concerns of the FAccT community.
  arXiv  Detail & Related papers  (2023-03-14T18:57:20Z)
• Industry-Academia Research Collaboration in Software Engineering: The Certus Model [13.021014899410684]
  Building scalable and effective research collaborations in software engineering is known to be challenging. This paper aims to understand what are the elements of a successful industry-academia collaboration that enable the culture of participative knowledge creation.
  arXiv  Detail & Related papers  (2022-04-23T10:16:23Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)
• Competency Model Approach to AI Literacy: Research-based Path from Initial Framework to Model [0.0]
  Research on AI Literacy could lead to an effective and practical platform for developing these skills. We propose and advocate for a pathway for developing AI Literacy as a pragmatic and useful tool for AI education.
  arXiv  Detail & Related papers  (2021-08-12T15:42:32Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• Learnings from Frontier Development Lab and SpaceML -- AI Accelerators for NASA and ESA [57.06643156253045]
  Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines. We perform a case study of the Frontier Development Lab (FDL), an AI accelerator under a public-private partnership from NASA and ESA. FDL research follows principled practices that are grounded in responsible development, conduct, and dissemination of AI research.
  arXiv  Detail & Related papers  (2020-11-09T21:23:03Z)

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.