Immersive Intelligent Tutoring System for Remedial Learning Using Virtual Learning Environment
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Alam, A., Ullah, S., and Ali, N. (2017). The effect of learning-based adaptivity on students’ performance in 3D-virtual learning environments. Institute of Electrical and Electronics Engineers (IEEE) Access, 6(2017), 3400-3407.
Brusilovsky, P. and Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education, 13(2–4), 159–172.
Choquet, C., and Iksal, S. (2007). Modélisation et construction de traces d'utilisation d'une activité d'apprentissage: Une approche langage pour la réingénierie d'un EIAH. Sciences et Technologies de l'Information et de la Communication pour l'Éducation et la Formation, 14(2007), 1-24.
Dai, C. Y., and Huang, D. H. (2015). Causal complexities to evaluate the effectiveness of remedial instruction. Journal of Business Research, 68(4), 894-899.
Das, B. K., and Pal, S. (2011). A framework of intelligent tutorial system to incorporate adaptive learning and assess the relative performance of adaptive learning system over general classroom learning. International Journal of Multimedia and Ubiquitous Engineering, 6(1), 43-54.
Fagerland, M. W. (2012). T-tests, non-parametric tests, and large studies—a paradox of statistical practice?. BMC Medical Research Methodology, 12(1), 1-7.
Feng, M., Heffernan, N. T., Heffernan, C., and Mani, M. (2009). Using mixed-effects modeling to analyze different grain-sized skill models in an intelligent tutoring system. IEEE Transactions on Learning Technologies, 2(2), 79-92.
Gong, Y., Beck, J. E., and Heffernan, N. T. (2011). How to construct more accurate student models: Comparing and optimizing knowledge tracing and performance factor analysis. International Journal of Artificial Intelligence in Education, 21(1-2), 27-46.
Humanante-Ramos, P. R., García-Peñalvo, F. J., and Conde-González, M. Á. (2015). Personal learning environments and online classrooms: An experience with university students. IEEE Revista Iberoamericana De Tecnologías Del Aprendizaje, 10(1), 26-32.
Juárez-Ramírez, R., Navarro-Almanza, R., Gomez-Tagle, Y., Licea, G., Huertas, C., and Quinto, G. (2013). Orchestrating an adaptive intelligent tutoring system: Towards integrating the user profile for learning improvement. Procedia-Social and Behavioral Sciences, 106(2013), 1986-1999.
Kavitha, R., Vijaya, A., and Saraswathi, D. (2012). Intelligent item assigning for classified learners in ITS using item response theory and point biserial correlation. 2012 International Conference on Computer Communication and Informatics (pp. 1-5). IEEE.
Khlaisang, J., and Songkram, N. (2019). Designing a virtual learning environment system for teaching twenty-first century skills to higher education students in ASEAN. Technology, Knowledge and Learning, 24(1), 41-63.
Klotz, J. (1998). A linked list for the Wilcoxon signed rank test. Journal of Nonparametric Statistics, 9(1), 87-93.
Kozhevnikov, M., Gurlitt, J., and Kozhevnikov, M. (2013). Learning relative motion concepts in immersive and non-immersive virtual environments. Journal of Science Education and Technology, 22(6), 952-962.
Lavigne, G., Ruiz, G. G., McAnally-Salas, L., and Sandoval, J. O. (2015). Log analysis in a virtual learning environment for engineering students. International Journal of Educational Technology in Higher Education, 12(3), 113-128.
Lin, C. C., Liu, Z. C., Chang, C. L., and Lin, Y. W. (2018). A genetic algorithm-based personalized remedial learning system for learning object-oriented concepts of Java. IEEE Transactions on Education, 62(4), 237-245.
Lin, H. C. K., Wu, C. H., and Hsueh, Y. P. (2014). The influence of using affective tutoring system in accounting remedial instruction on learning performance and usability. Computers in Human Behavior, 41(2014), 514-522.
Nkambou, R., Mizoguchi, R., and Bourdeau, J. (Eds.). (2010). Advances in intelligent tutoring systems. Springer Science and Business Media,308, 1-408.
Reisslein, J., Reisslein, M., and Seeling, P. (2006). Comparing static fading with adaptive fading to independent problem solving: The impact on the achievement and attitudes of high school students learning electrical circuit analysis. Journal of Engineering Education, 95(3), 217-226.
Rosmansyah, Y., Achiruzaman, M., and Hardi, A. B. (2019). A 3D multiuser virtual learning environment for online training of agriculture surveyors. Journal of Information Technology Education: Research, 18(2019), 481-507.
Ruxton, G. D. (2006). The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test. Behavioral Ecology, 17(4), 688-690.
Salden, R. J., Aleven, V. A., Renkl, A., and Schwonke, R. (2009). Worked examples and tutored problem solving: redundant or synergistic forms of support?. Topics in Cognitive Science, 1(1), 203-213.
Samuelis, L. (2007). Notes on the components for intelligent tutoring systems. Acta Polytechnica Hungarica, 4(2), 77-85.
Scott, E., Soria, A., and Campo, M. (2016). Adaptive 3D virtual learning environments—A review of the literature. IEEE Transactions on Learning Technologies, 10(3), 262-276.
Settouti, L. S., Prié, Y., Marty, J. C., and Mille, A. (2007). Vers des Systèmes à Base de Traces modélisées pour les EIAH. Rapport de Recherche RR-LIRIS-2007, 16(2007), 1-30.
Skudder, B., and Luxton-Reilly, A. (2014). Worked examples in computer science. Proceedings of the Sixteenth Australasian Computing Education Conference-Volume, 148(2014), 59-64.
Van De Sande, B. (2013). Properties of the Bayesian knowledge tracing model. Journal of Educational Data Mining, 5(2), 1-10.
DOI: https://doi.org/10.17509/ijost.v6i3.38954
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