Low-cost desktop learning factory to support the teaching of artificial intelligence

被引:0
作者
Orozco, Eduardo [1 ]
Cardenas, Paulo C. [2 ]
Lopez, Jesus A. [1 ]
Rodriguez, Cinthia K. [1 ]
机构
[1] Univ Autonoma Occidente, Dept Automat & Elect, Cali, Colombia
[2] Univ Autonoma Manizales, Dept Phys & Math, Manizales, Colombia
来源
HARDWAREX | 2024年 / 18卷
关键词
Machine learning; Artificial intelligence; Education k-12; Teaching strategy; TECHNOLOGY; SCIENCE;
D O I
10.1016/j.ohx.2024.e00528
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The following document details low-cost hardware and open -source available software tools that can be combined to support active teaching methodologies like Problem -Based Learning (PBL) and incorporate work -oriented technological skills in students. This proposal presents a prototype of Open Educational Resources (OER) that integrates software and hardware tools for the specific purpose of facilitating instruction in Artificial Intelligence. The hardware consists of affordable electronic devices, including an Arduino board, servo motors, sensors, a relay and a motor, all integrated into a scaled conveyor belt. On the other hand, open software was used to implement an image classification program with different features (shape, color, size, among others). The exact construction steps, circuits, and code are presented in detail and should encourage other scientists to replicate the experimental setup, especially if they are looking for experimental teaching of artificial intelligence, since the system allows object classification using the machine learning paradigm to facilitate the teaching of artificial intelligence concepts with computer vision concepts.
引用
收藏
页数:23
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共 15 条
  • [1] Emerging Hardware Prototyping Technologies as Tools for Learning
    Al-Masri, Eyhab
    Kabu, Shubham
    Dixith, Poornima
    [J]. IEEE ACCESS, 2020, 8 (08): : 80207 - 80217
  • [2] Inspection and grading of agricultural and food products by computer vision systems - a review
    Brosnan, T
    Sun, DW
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2002, 36 (2-3) : 193 - 213
  • [3] Active learning increases student performance in science, engineering, and mathematics
    Freeman, Scott
    Eddy, Sarah L.
    McDonough, Miles
    Smith, Michelle K.
    Okoroafor, Nnadozie
    Jordt, Hannah
    Wenderoth, Mary Pat
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2014, 111 (23) : 8410 - 8415
  • [4] Guanhao Yang, 2021, 2021 IEEE 21st Annual Wireless and Microwave Technology Conference (WAMICON), P11, DOI 10.1109/ICSIP52628.2021.9688725
  • [5] Hiwonder JetMax, 2022, The AI vision robotic arm for endless creativity
  • [6] Active Learning Augmented Reality for STEAM Education-A Case Study
    Jesionkowska, Joanna
    Wild, Fridolin
    Deval, Yann
    [J]. EDUCATION SCIENCES, 2020, 10 (08): : 1 - 15
  • [7] EUROPA: A Case Study for Teaching Sensors, Data Acquisition and Robotics via a ROS-Based Educational Robot
    Karalekas, Georgios
    Vologiannidis, Stavros
    Department, John Kalomiros
    [J]. SENSORS, 2020, 20 (09)
  • [8] LEGO Education, 2023, MINDSTORMS EV3 core set computer integrated manufacturing
  • [9] Using Robotics to Enhance Active Learning in Mathematics: A Multi-Scenario Study
    Lopez-Caudana, Edgar
    Soledad Ramirez-Montoya, Maria
    Martinez-Perez, Sandra
    Rodriguez-Abitia, Guillermo
    [J]. MATHEMATICS, 2020, 8 (12) : 1 - 21
  • [10] Comparison of high-technology active learning and low-technology active learning classrooms
    Nicol, Adelheid A. M.
    Owens, Soo M.
    Le Coze, Stephanie S. C. L.
    MacIntyre, Allister
    Eastwood, Christina
    [J]. ACTIVE LEARNING IN HIGHER EDUCATION, 2018, 19 (03) : 253 - 265