A Collaborative and Pattern-Based Training Approach to Knowledge Acquisition and Decision-Making During the Design of Software Architectures Courses: A Case Study
- URL: http://arxiv.org/abs/2603.11904v1
- Date: Thu, 12 Mar 2026 13:18:13 GMT
- Title: A Collaborative and Pattern-Based Training Approach to Knowledge Acquisition and Decision-Making During the Design of Software Architectures Courses: A Case Study
- Abstract summary: This article describes a collaborative learning experience on Software Architecture (SA) between Universidad del Cauca (UNICAUCA) in Colombia and Universidad Nacional de la Plata (UNPL) in Argentina.<n>The goal was to apply and evaluate training patterns, identifying effective practices for replication in other contexts.
- Score: 0.08496348835248901
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This article describes a collaborative learning experience on Software Architecture (SA) between Universidad del Cauca (UNICAUCA) in Colombia and Universidad Nacional de la Plata (UNPL) in Argentina. The goal was to apply and evaluate training patterns, identifying effective practices for replication in other contexts. During the planning phase, both universities compared learning objectives, curricula, and teaching strategies to find common ground for improving student training. Selected training patterns were implemented, and their impact on professors and students was measured. As an integrating activity, a global development experience was carried out in the final part of the course, merging the work teams of the two educational institutions in a development iteration. The evaluation of this experience focused on the competencies achieved through the training patterns, their perceived usefulness, and ease of use based on the Technology Acceptance Model (TAM). The training addressed industry needs for software architecture design skills despite challenges such as the abstract nature of architectures, prerequisite knowledge, difficulty in recreating realistic project environments, team collaboration challenges, and resource limitations. A catalog of training patterns was proposed to provide quality training. These patterns help simulate industry-like environments and structure architectural knowledge for incremental learning. The ability to make architectural decisions is developed over time and through multiple project experiences, emphasizing the need for practical, well-structured training programs.
Related papers
- Knowledge-Centric Agents for Workflow Generation [73.4340391112949]
We propose a knowledge-centric framework that learns to invert, inject, and infer with knowledge across multiple abstraction levels.<n>Our method produces with richer node diversity, more coherent structures, and higher execution success rates than existing systems.
arXiv Detail & Related papers (2026-07-17T11:03:01Z) - Student Competency Assessment and Presentation Methods Based on Algorithm Courses [4.332793252500841]
competency-based education, which integrates knowledge, skills, and dispositions, has become pivotal in computer science education.<n>The research aims to analyze learning behavior patterns, design methods for competency assessment in algorithm courses, and evaluate the difficulty of course experiments to inform curriculum design.
arXiv Detail & Related papers (2026-05-29T17:03:59Z) - Reshaping Undergraduate Computer Science Education in the Generative AI Era [85.78707817763434]
Generative AI represents a turning point for Computer Science (CS) education.<n>To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts.<n>We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI.
arXiv Detail & Related papers (2026-05-02T05:26:24Z) - Beyond the Code: A Multi-Modal Assessment Strategy for Fostering Professional Competencies via Introductory Programming Projects [1.8810916321241071]
This paper reports on a "Fundamentals of Programming" course that used a Project-Based Learning framework to develop a 2D "Maze Runner"-style game.<n>Our findings suggest that this multi-dimensional approach improves student retention of programming fundamentals.
arXiv Detail & Related papers (2026-03-19T10:42:34Z) - Exploring the Modular Integration of "AI + Architecture" Pedagogy in Undergraduate Design Education: A Case Study of Architectural Design III/IV Courses at Zhejiang University [1.0438371426162936]
This study investigates AI integration in architectural education through a teaching experiment in Zhejiang University's 2024-25 grade three undergraduate design studio.<n>Adopting a dual-module framework, the course introduced deep learning models, LLMs, AIGC, LoRA, and ComfyUI.<n>Findings demonstrate the effectiveness of phased guidance, balanced technical-ethical approaches, and institutional support.
arXiv Detail & Related papers (2025-12-14T02:38:21Z) - Learning to Code with Context: A Study-Based Approach [0.28675177318965045]
The rapid emergence of generative AI tools is transforming the way software is developed.<n>Project-based courses offer an effective environment to explore and evaluate the integration of AI assistance into real-world development practices.<n>This paper presents our approach and a user study conducted within a university programming project in which students collaboratively developed computer games.
arXiv Detail & Related papers (2025-12-04T20:40:36Z) - The Imperfect Learner: Incorporating Developmental Trajectories in Memory-based Student Simulation [55.722188569369656]
This paper introduces a novel framework for memory-based student simulation.<n>It incorporates developmental trajectories through a hierarchical memory mechanism with structured knowledge representation.<n>In practice, we implement a curriculum-aligned simulator grounded on the Next Generation Science Standards.
arXiv Detail & Related papers (2025-11-08T08:05:43Z) - Large Language Models in Architecture Studio: A Framework for Learning Outcomes [0.0]
The study explores the role of large language models (LLMs) in the context of the architectural design studio.<n>The main challenges include managing student autonomy, tensions in peer feedback, and the difficulty of balancing the transmission of technical knowledge with the stimulation of creativity in teaching.
arXiv Detail & Related papers (2025-10-08T02:51:22Z) - Semi-Automated Design of Data-Intensive Architectures [49.1574468325115]
This paper introduces a development methodology for data-intensive architectures.<n>It guides architects in (i) designing a suitable architecture for their specific application scenario, and (ii) selecting an appropriate set of concrete systems to implement the application.<n>We show that the description languages we adopt can capture the key aspects of data-intensive architectures proposed by researchers and practitioners.
arXiv Detail & Related papers (2025-03-21T16:01:11Z) - Training a Generally Curious Agent [77.61142660542599]
Paprika is a fine-tuning approach that enables language models to develop general decision-making capabilities.<n>Paprika teaches models to explore and adapt their behavior on a new task based on environment feedback in-context without more gradient updates.<n>Results suggest a promising path towards AI systems that can autonomously solve sequential decision-making problems.
arXiv Detail & Related papers (2025-02-24T18:56:58Z) - Towards a case-based learning approach to support software architecture
education [0.0]
The main goal of this paper is to present a case-based learning approach that guides the development of learning objectives.
The results show that it can leverage the ways to adequately explore cases for educational purposes while also motivating instructors and students to the software architecture education.
arXiv Detail & Related papers (2022-09-12T18:29:20Z) - Policy Architectures for Compositional Generalization in Control [71.61675703776628]
We introduce a framework for modeling entity-based compositional structure in tasks.
Our policies are flexible and can be trained end-to-end without requiring any action primitives.
arXiv Detail & Related papers (2022-03-10T06:44:24Z) - 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) - Framework for an Integrated Learning Block with CDIO-led Engineering
Education [0.2578242050187029]
The School of Mechanical and Electrical Engineering Sheridan maintains a curriculum that is deeply rooted in skills-based learning, experiential learning, and engineering design.
An important challenge still impeding our students knowledge acquisition is the perception that program courses have disjointed learning outcomes.
One feasible approach to tackle this pedagogical challenge is to combine various courses into an integrated learning block (ILB) having a unified mission and objective.
arXiv Detail & Related papers (2020-06-04T22:00:58Z)
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.