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All issues Volume 35 (2020) ITM Web Conf., 35 (2020) 01008 References
Open Access
Issue
ITM Web Conf.
Volume 35, 2020
International Forum “IT-Technologies for Engineering Education: New Trends and Implementing Experience” (ITEE-2019)
Article Number 01008
Number of page(s) 12
Section Engineering Education Technology Based on Using Digital Resources
DOI https://doi.org/10.1051/itmconf/20203501008
Published online 09 December 2020
  1. J. Trevelyan, Transitioning to engineering practice, European Journal of Engineering Education, 44(6), pp. 821-837 (2019). https://doi.org/10.1080/03043797.2019.1681631. [CrossRef] [Google Scholar]
  2. R. Razdan, R. Polanco, Z. Ackerman, X. Vidot, D. Razdan, GANDALF: A real-world solution to the “soft Skills” problem for engineering careers, IEEE Technology and Engineering Management Conference, TEMSCON (2019). 8813665. DOI: 10.1109/TEMSCON.2019.8813665. [Google Scholar]
  3. N.I. Sidnyaev, Modern problems of elite engineering education, Mechanical engineering and engineering education, № 3, 64–74 (2014). [Google Scholar]
  4. D.V. Kurennov, E.I. Katz, G.L. Kovács, Swept volume evaluation using the BSP-dexel representation for the 5-axis CNC machining simulation, Pollack Periodica, 14(1), pp. 59-66 (2019). https://doi.org/10.1556/606.2019.14.1.6. [CrossRef] [Google Scholar]
  5. R. Gayanov, K. Mironov, R. Mukhametshin, A. Vokhmintsev, D. Kurennov, Transportation of small objects by robotic throwing and catching: applying genetic programming for trajectory estimation, IFAC-PapersOnLine, Volume 51, Issue 30, pp. 533-537 (2018). https://doi.org/10.1016/j.ifacol.2018.11.271. [CrossRef] [Google Scholar]
  6. O.V. Savchenko, D.V. Kurennov, Description of the technique of pattern recognition and neural network formation in the conditions of multistage information processing, Information Technologies for Intelligent Decision Making Support (ITIDS’2016) Proceedings of the 4th International Conference, pp. 181-184 (2016). [Google Scholar]
  7. S. Cass, The Top Programming Languages 2019 (2019) Retrieved from https://spectrum.ieee.org/computing/software/the-top-programming-languages-2019. [Google Scholar]
  8. R. Fraanje, T. Koreneef, A. Le Mair, S. de Jong, Python in robotics and mechatronics education, 2016 11th France-Japan & 9th Europe-Asia Congress on Mechatronics (MECATRONICS)/17th International Conference on Research and Education in Mechatronics (REM), pp. 014-019. IEEE June (2016). DOI: 10.1109/MECATRONICS.2016.7547108 [Google Scholar]
  9. A.B. Raju, S. Annigeri, Computing in engineering education: The current scenario, 2014 International Conference on Contemporary Computing and Informatics (IC3I), pp. 130-134. IEEE November (2014). DOI: 10.1109/IC3I.2014.7019770. [Google Scholar]
  10. M. Nedaei, F. Radmehr, M. Drake, Exploring engineering undergraduate students’ attitudes toward mathematical problem posing, Journal of Professional Issues in Engineering Education and Practice 145(4), 04019009 (2019). DOI: 10.1061/(ASCE)EI.1943-5541.0000418. [CrossRef] [Google Scholar]
  11. E.V. Kuzmina, N.G. Pyankova, N.Y. Naryzhnaya, Development of creative potential of students of engineering specialties within the framework of mathematical and information disciplines, D. Solovev (eds), Smart Technologies and Innovations in Design for Control of Technological Processes and Objects: Economy and Production. FarEastСon 2018, Smart Innovation, Systems and Technologies, vol 139. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18553-4_7. [Google Scholar]
  12. D. Dias Rasteiro, V. Gayoso Martinez, C. Caridade, J. Martin-Vaquero, A. QueirugaDios, Changing teaching: Competencies versus contents, IEEE Global Engineering Education Conference, EDUCON, vol. 2018-April, 23 May 2018, pp. 1761-1765 (2018). DOI: 10.1109/EDUCON.2018.8363447. [Google Scholar]
  13. A. Munns, A study of competence in mathematics and mechanics in an engineering curriculum, European Journal of Engineering Education, 42 (6), pp. 1062-1075 (2017). https://doi.org/10.1080/03043797.2016.1259292. [CrossRef] [Google Scholar]
  14. J.A. Weliwita, S. Witharana, Amalgamate mathematical methods and engineering curriculum to uplift innovations, Advances in Science and Engineering Technology International Conferences, ASET 20188 June 2018, pp. 1-5 (2018). DOI: 10.1109/ICASET.2018.8376935. [Google Scholar]
  15. G.V. Bard, Sage for Undergraduates, The American Mathematical Society, p. 362 (2015). ISBN-13: 978-1470411114. [Google Scholar]
  16. P. Zimmermann, A. Casamayou, N. Cohen, G. Connan, T. Dumont, L. Fousse, F. Maltey, M. Meulien, M. Mezzarobba, C. Pernet, N. M. Thiéry, E. Bray, J. Cremona, M. Forets, A. Ghitza, H. Thomas, Computational Mathematics with SageMath. http://sagebook.gforge.inria.fr. [Google Scholar]
  17. C. Vieira, A. J. Magana, A. Roy, M.L. Falk, Student explanations in the context of computational science and engineering education, Cognition and Instruction, 37(2), pp. 201-231 (2019). https://doi.org/10.1080/07370008.2018.1539738. [CrossRef] [Google Scholar]
  18. M.I. Szávuly, Á. Tóos, R. Barabás, B. Szilágyi, From modeling to virtual laboratory development of a continuous binary distillation column for engineering education using MATLAB and LabVIEW, Computer Applications in Engineering Education, 27(5), 1019-1029 (2019). https://doi.org/10.1002/cae.22119. [CrossRef] [Google Scholar]
  19. J.L. Saorín, J. de la Torre-Cantero, D.M. Díaz, V. López-Chao, Cloud-based collaborative 3D modeling to train engineers for the Industry 4.0, Applied Sciences (Switzerland), 9(21), November 2019, 4559 (2019). https://doi.org/10.3390/app9214559. [CrossRef] [Google Scholar]
  20. C. Carbonell-Carrera, J.L. Saorin, D. Melian-Diaz, J. de la Torre-Cantero, Enhancing creative thinking in STEM with 3D CAD modelling, Sustainability (Switzerland), 11 (21), November 2019, p. 6036 (2019). https://doi.org/10.3390/su11216036. [CrossRef] [Google Scholar]
  21. M. Planchard, 3D CAD: A plus for STEM education, Engineering Design Graphics Journal, 71 (2), pp. 1-4 (2007). [Google Scholar]

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