Análise e modelagem de jogos digitais: relato de uma experiência educacional utilizando metodologias ativas em um grupo multidisciplinar

• URL: http://arxiv.org/abs/2311.14704v2
• Date: Sun, 1 Sep 2024 13:21:34 GMT
• Title: Análise e modelagem de jogos digitais: relato de uma experiência educacional utilizando metodologias ativas em um grupo multidisciplinar
• Authors: David de Oliveira Lemes, Ezequiel França dos Santos, Eduardo Romanek, Celso Fujimoto, Adriano Felix Valente,
• Abstract summary: The traditional teaching of software engineering is focused on technical skills. Problem-Based Learning (PBL) brings real market scenarios into the classroom. This article reports on the experience in the course, presenting concepts and results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The traditional teaching of software engineering is focused on technical skills. Active strategies, where students experience content and interact with reality, are effective. The market demands new skills in the digital transformation, dealing with the complexity of modeling businesses and the interconnection between people, systems, and technologies. The transition to active methodologies, such as Problem-Based Learning (PBL), brings real market scenarios into the classroom. This article reports on the experience in the course, presenting concepts and results.

Related papers

• Hands-on STEM Learning Experiences using Digital Technologies [0.0]
  The objective of this work is to present an account of our experiences and activities carried out in Italian schools with this novel approach. The selection of projects and experiences discussed --in which students develop a range of core competencies such as collaboration, creativity, critical thinking, experimentation, prototyping, communication and problem-solving.
  arXiv  Detail & Related papers  (2024-07-17T10:09:48Z)
• HEMM: Holistic Evaluation of Multimodal Foundation Models [91.60364024897653]
  Multimodal foundation models can holistically process text alongside images, video, audio, and other sensory modalities. It is challenging to characterize and study progress in multimodal foundation models, given the range of possible modeling decisions, tasks, and domains.
  arXiv  Detail & Related papers  (2024-07-03T18:00:48Z)
• Bringing active learning, experimentation, and student-created videos in engineering: A study about teaching electronics and physical computing integrating online and mobile learning [0.0]
  The main aim of this study was to create an AL methodology to learn electronics, physical computing (PhyC), programming, and basic robotics in engineering through hands-on activities and active experimentation in online environments. The methodology was conceived using the guidelines of the Integrated Course Design Model (ICDM) and in some courses combining mobile and online learning with an Android app. The outcomes indicate a good perception of the PhyC and programming activities by the students and suggest that these influence motivation, self-efficacy, reduction of anxiety, and improvement of academic performance in the courses.
  arXiv  Detail & Related papers  (2024-06-02T23:26:27Z)
• Foundation Models for Decision Making: Problems, Methods, and Opportunities [124.79381732197649]
  Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. New paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems.
  arXiv  Detail & Related papers  (2023-03-07T18:44:07Z)
• Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems [46.760568562468606]
  Few-shot meta-learning optimize learning algorithms that can efficiently adapt to new tasks quickly. This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications.
  arXiv  Detail & Related papers  (2022-10-03T17:15:36Z)
• Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions [68.6358773622615]
  This paper provides an overview of the computational and theoretical foundations of multimodal machine learning. We propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches.
  arXiv  Detail & Related papers  (2022-09-07T19:21:19Z)
• Panoramic Learning with A Standardized Machine Learning Formalism [116.34627789412102]
  This paper presents a standardized equation of the learning objective, that offers a unifying understanding of diverse ML algorithms. It also provides guidance for mechanic design of new ML solutions, and serves as a promising vehicle towards panoramic learning with all experiences.
  arXiv  Detail & Related papers  (2021-08-17T17:44:38Z)
• Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework [23.475874929905192]
  We propose a novel contrastive multi-party learning framework for knowledge refinement and sharing. The proposed scheme achieves significant improvement in model performance in a variety of scenarios.
  arXiv  Detail & Related papers  (2021-04-14T07:33:48Z)
• Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation [32.74513588794863]
  Value Card is an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation. Our results suggest that the use of the Value Cards toolkit can improve students' understanding of both the technical definitions and trade-offs of performance metrics.
  arXiv  Detail & Related papers  (2020-10-22T03:27:19Z)
• Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology [53.063411515511056]
  We propose a process model for the development of machine learning applications. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications.
  arXiv  Detail & Related papers  (2020-03-11T08:25:49Z)

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.