Open Access
Issue
E3S Web Conf.
Volume 317, 2021
The 6th International Conference on Energy, Environment, Epidemiology, and Information System (ICENIS 2021)
Article Number 05027
Number of page(s) 10
Section Information System Management and Environment
DOI https://doi.org/10.1051/e3sconf/202131705027
Published online 05 November 2021
  1. Direktorat Jenderal Pendidikan Dasar dan Menengah (Ditjen Dikdasmen), Grand Design Pengembangan Teaching Factory dan Technopark di SMK (Ditjen Dikdasmen, Jakarta, 2016) [Google Scholar]
  2. Baharuddin, J. Dalle, Interactive courseware for supporting learners competency in practical skills, Turkish Online J. of Edu.Tech, 16, 88-99, (2017) [Google Scholar]
  3. N.W.A. Majid, T. Ridwan,, A. Fauzi, R. Hikmawan, Integrating of E-learning to Improve Students Competence in Vocational School, in proc. of Int. Conf. on Technical and Vocational Education and Training, ICTVET, Feb. 2019, Bandung, Indonesia(2019) [Google Scholar]
  4. A. Keleş, R. Ocak, A. Keleş, A. Gülcü, ZOSMAT: Web-based Intelligent Tutoring System for Teaching–Learning Process, Expert Systems with Applications, 36, 1229-1239 (2009) [Google Scholar]
  5. A. Ramírez-Noriega, R. Juárez-Ramírez, Y. Martínez-Ramírez, Evaluation module based on Bayesian Networks to Intelligent Tutoring Systems, Int. J. of Information Management, 37, 1488-1498 (2017) [Google Scholar]
  6. J.A. Kulik, J.D. Fletcher, Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review, Review of Educational Research, 86, 42–78 (2016) [Google Scholar]
  7. J. Han, W. Zhao, Q. Jiang, M. Oubibi, X. Hu, Intelligent Tutoring System Trends 2006–2018: A Literature Review, in Proceedings of 8th Int. Conf. on Educational Innovation through Technology, EITT, 27–31 Oct. 2019, Mississippi, USA (2019) [Google Scholar]
  8. Y. He, S.C. Hui, T.T. Quan, Automatic Summary Assessment for Intelligent Tutoring Systems, Computers & Education, 53, 890-899, (2009) [Google Scholar]
  9. K. VanLehn, The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems, Educational Psychologist, 46, 197–221, (2011) [Google Scholar]
  10. K. Kularbphettong, P. Kedsiribut, P. Roonrakwit, Developing an Adaptive Web-based Intelligent Tutoring System Using Mastery Learning Technique, Procedia - Social and Behavioral Sciences, 191, 686-691 (2015) [Google Scholar]
  11. A. Karaci, H.I. Akyuz, G. Bilgici, Effects of Web-based Intelligent Tutoring Systems on Academic Achievement and Retention, Int. J. of Computer Applications, 181, 36-41 (2018) [Google Scholar]
  12. S.D. de Carvalho, F.R. de Melo, E.L. Flôres, S.R. Pires, L.F.B. Loja, Intelligent tutoring system using expert knowledge and Kohonen maps with automated training, Neural Comput. & Appl, 32, 13577–13589 (2020) [Google Scholar]
  13. A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari, D.B. Rubin, Bayesian data analysis: third edition (CRC Press Taylor & Francis Group, Florida, 2013) [Google Scholar]
  14. B.G. Marcot, T.D. Penman, Advances in Bayesian network modelling: Integration of modelling technologies, Environmental Modelling & Software, 111, 386-393 (2019) [Google Scholar]
  15. M.A. Javidian, Z. Wang, L. Lu, M. Valtorta, On a hypergraph probabilistic graphical model, Annals of Mathematics and Artificial Intelligence, 88, 1003–1033 (2020) [Google Scholar]
  16. L. Huang, G. Cai, H. Yuan, J. Chen, A hybrid approach for identifying the structure of a Bayesian network model, Expert Systems with Applications, 131, 308-320 (2019) [Google Scholar]
  17. B.G. Marcot, A.M. Hanea, What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?, Computational Statistics, 1-23 (2020) [Google Scholar]
  18. J. Xu, Y. Zhang, D. Miao, Three-way confusion matrix for classification: A measure driven view, Information Sciences, 507, 772-794 (2020) [Google Scholar]
  19. R.R. Hake, Interactive engagement vs traditional methods: six-thousand student survey of mechanics test data for introductory physics courses, American Journal of Physics, 66, 64–74 (1998) [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.