A Collaborative and Pattern-Based Training Approach to Knowledge Acquisition and Decision-Making During the Design of Software Architectures Courses: A Case Study

2026-03-12

AI Summary

This study addresses the challenges in software architecture education—such as high abstraction, demanding prerequisite knowledge, lack of authentic project contexts, and limited resources—that hinder students’ practical competencies. To overcome these issues, the authors propose and empirically validate a reusable catalog of software architecture training patterns, collaboratively developed by universities in Colombia and Argentina. The approach integrates instructional patterns, a collaborative learning framework, and curriculum alignment strategies to orchestrate cross-institutional, industry-simulated development projects. Evaluation grounded in the Technology Acceptance Model (TAM) demonstrates that this method significantly enhances students’ architectural decision-making and design capabilities. The findings confirm its practicality and scalability under resource-constrained conditions, offering a novel paradigm for high-quality, inter-institutional software architecture education.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Deep Neural Architectures and Foundation Models
• Application Domains: Software Engineering

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Software Architecture
• Knowledge Acquisition
• Architectural Decision-Making
• Training Patterns
• Collaborative Learning

Innovation

• training patterns
• collaborative learning
• software architecture education
• Technology Acceptance Model
• architectural decision-making