Connecting CS1 with Student’s Careers Through Multidisciplinary Projects. Case of Study: Material Selection Following the Ashby Methodology

  • Bruno Paucar
  • Giovanny Chunga
  • Natalia Lopez
  • Clotario Tapia
  • Miguel RealpeEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1051)


This paper describes the implementation of an open-source software developed using Python, which facilitates the materials selection process commonly used in engineering. This software has been developed by non-CS students (Materials Engineering, Food Engineering and Chemistry Engineering), as a project course of their 1st-year cross-curricular course of CS1 (“Programming Fundamentals”), in order to connect their CS1 learning process with core subjects related to their careers, aiming to motivate both, the use of computer programming in their personal development and also, their interest in their professional career. The program developed allows choosing between different types of materials, based on specific characteristics required by the user; furthermore, this program enables the visualization of the Michael Ashby methodology for materials selection, which allows non-CS students to solve a problem related to their career, while it gives upper-level students a new tool to learn in class. The dataset used covers approximately 10000 distinct materials, classified by its features as ceramics, metals, polymers, wood/natural materials, pure elements and other advanced engineering materials. As a part of the outcome of this project, a public access repository has been created containing the implemented algorithms and the dataset used. The code developed can be modified and reused under license “GNU General Public License”. Finally, a report on the perception of non-CS students taking CS1 and the perception of upper-level students taking “Material selection” subject is described and analyzed.


CS1 Ashby methodology Materials selection Computer science education Project Based Learning PjBL Self-efficacy 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bruno Paucar
    • 1
  • Giovanny Chunga
    • 1
  • Natalia Lopez
    • 1
  • Clotario Tapia
    • 1
  • Miguel Realpe
    • 1
    Email author
  1. 1.ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOLGuayaquilEcuador

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