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 08002
Number of page(s) 11
Section International Integration in the Field of Digital Engineering Education
DOI https://doi.org/10.1051/itmconf/20203508002
Published online 09 December 2020
  1. P.N. Thach, B.N. Phuong, C.C. Dung, L.H. Van & P.T.H. Diep, A Dynamic Fuzzy Multiple Criteria Decision-making Approach for Lecturer Performance Evaluation, Journal of Management Information and Decision Sciences (2019) [Google Scholar]
  2. J.S. Jeong & D. González-Gómez, Prioritizing Elements of Science Education for Sustainable Development with the MCDA-FDEMATEL Method Using the Flipped ELearning Scheme, Sustainability, 11(11), p. 3079 (2019) [CrossRef] [Google Scholar]
  3. A. Al-Shagran, Assessment of E-learning Systems: A Systems Engineering Approach System (2017) [Google Scholar]
  4. Z. Jianzhong, On cdio model under learning by doing strategy, Higher Education of Engineering, 3, pp. 1-6 (2008) [Google Scholar]
  5. K.F. Berggren, D. Brodeur, E.F. Crawley, I. Ingemarsson, W.T. Litant, J. Malmqvist & S. Östlund, CDIO: An international initiative for reforming engineering education, World Transactions on Engineering and Technology Education, 2(1), pp. 49-52 (2003) [Google Scholar]
  6. E.F. Crawley, J. Malmqvist, W.A. Lucas & D.R. Brodeur, The CDIO syllabus v2. 0. An updated statement of goals for engineering education, Proceedings of 7th international CDIO conference, Copenhagen, Denmark (2011) [Google Scholar]
  7. K. Edström & A. Kolmos, PBL and CDIO: complementary models for engineering education development, European Journal of Engineering Education, 39(5), pp. 539-555 (2014) [CrossRef] [Google Scholar]
  8. J. Bankel, K.F. Berggren, M. Engström, I. Wiklund, E.F. Crawley, D.H. Soderholm, ... & S. Östlund, Benchmarking engineering curricula with the CDIO syllabus, International journal of engineering education, 21(1), pp. 121-133 (2005) [Google Scholar]
  9. A. Al-Shagran, Towards a Research Road Map for Assessment of E-learning Systems: A Systems Engineering Approach, Proceedings of the International Conference on eLearning, e-Business, Enterprise Information Systems, and e-Government (EEE) (p. 54), The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2016) [Google Scholar]
  10. R. Dehbi, Model driven engineering applied in e-learning development process: advanced comparative study with ROC multi-criteria analysis, International Journal of Online Pedagogy and Course Design (IJOPCD), 7(1), pp. 15-32 (2017) [CrossRef] [Google Scholar]
  11. M. Rüth & K. Kaspar, The E-Learning Setting Circle: First Steps toward Theory Development in E-Learning Research, Electronic Journal of e-Learning, 15(1), pp. 94103 (2017) [Google Scholar]
  12. D. Vlachopoulos, Assuring quality in e-learning course design: The roadmap, The International Review Of Research In Open And Distributed Learning, 17(6) (2016) [CrossRef] [Google Scholar]
  13. T. Rüütmann & H. Kipper, Klagenfurt School of Engineering Pedagogy by Adolf Melezinek as the Basis of Teaching Engineering, International Journal of Engineering Pedagogy (iJEP), 6(3), pp. 10-18 (2016) [CrossRef] [Google Scholar]
  14. S. Farid, R. Ahmad, M. Alam, A. Akbar & V. Chang, A sustainable quality assessment model for the information delivery in E-learning systems, Information Discovery and Delivery, 46(1), pp. 1-25 (2018) [CrossRef] [Google Scholar]
  15. C. Thune, Standards and guidelines for quality assurance in the European Higher Education Area, European Association for Quality Assurance in the European Higher Education (2005) [Google Scholar]
  16. European Association for Quality Assurance in Higher Education (ENQA), Standards and guidelines for quality assurance in the European Higher Education Area (ESG). Brussels: ENQA (2015) [Google Scholar]
  17. A. Krasovsky, S. Vasyukov & O. Miseyuk, The System Approach for the Application of Measuring Instruments and Computer Modeling Tools for the Electrical Laboratory for the Bauman Moscow State Technical Univercity, 2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), pp. 1-6, IEEE November (2018) [Google Scholar]
  18. V.B. Kamat & J.K. Kittur, Devising smart strategic framework for assessment of quality in engineering education, International Journal of System Assurance Engineering and Management, 10(6), pp. 1403-1428 (2019) [Google Scholar]
  19. T.D. Margaryan & N.G. Alyavdina, NEW APPROACES IN TEACHING ENGLISH AT BAUMAN UNIVERSITY, Editorial board, p. 40 (2017) [Google Scholar]
  20. A. Krasovsky, S. Vasyukov & O. Miseyuk, The System Approach for the Application of Measuring Instruments and Computer Modeling Tools for the Electrical Laboratory for the Bauman Moscow State Technical Univercity, 2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), pp. 1-6, IEEE, November (2018) [Google Scholar]
  21. M. Zare, C. Pahl, H. Rahnama, M. Nilashi, A. Mardani, O. Ibrahim & H. Ahmadi, Multi-criteria decision making approach in E-learning: A systematic review and classification, Applied Soft Computing, 45, pp. 108-128 (2016) [CrossRef] [Google Scholar]
  22. M.N. Filipe, F.A. Ferreira & S.P. Santos, A multiple criteria information system for pedagogical evaluation and professional development of teachers, Journal of the Operational Research Society, 66(11), pp. 1769-1782 (2015) [CrossRef] [Google Scholar]
  23. J. Vargas, M.C. Moreno & L. Isaza, Evaluation Model Based on Artificial Neural Networks for the use and Appropriation of Information and Communication Technologies in Higher Education Teachers, International Journal of Applied Engineering Research, 13(12), pp. 10169-10174 (2018) [Google Scholar]
  24. L.A. Kayumova, L.I. Savva, A.L. Soldatchenko, R.M. Sirazetdinov & L.G. Akhmetov, The Technology of Forming of Innovative Content for Engineering Education, International Journal of Environmental and Science Education, 11(9), pp. 3029-3040 (2016) [Google Scholar]
  25. I. Aouadni & A. Rebai, Decision support system based on genetic algorithm and multicriteria satisfaction analysis (MUSA) method for measuring job satisfaction, Annals of Operations Research, 256(1), pp. 3-20 (2017) [CrossRef] [Google Scholar]
  26. J. Kook, Criteria for selecting the appropriate mobile application development platform for higher education, INSTRUCTIONAL TECHNOLOGY, 73 (2015) [Google Scholar]
  27. E. Kurilovas, J. Kurilova & T. Andruskevic, On suitability index to create optimal personalised learning packages, International Conference on Information and Software Technologies, October 2016, pp. 479-490, Springer, Cham (2016) [CrossRef] [Google Scholar]
  28. E. Kurilovas & I. Vinogradova, Improved fuzzy AHP methodology for evaluating quality of distance learning courses, International Journal of Engineering Education, 32(4), pp. 1618-1624 (2016) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.