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NTIS 바로가기한국융합학회논문지 = Journal of the Korea Convergence Society, v.11 no.3, 2020년, pp.67 - 75
The 'level of difficulty' is one of the major factors for learners when selecting learning contents. However, the criteria for the difficulty level is mostly defined by the contents providers. This approach does not support the personalized education which should consider the abilities and environme...
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C. H. Kim, H. D. Ko & B. K. Kim. (2005). Item Difficulty Analysis of Learning Contents Based on SCORM. Proceedings of the Korean Information Science Society Conference. (pp. 358-360). Seoul : KIISE.
A. Baylari & G. A. Montazer. (2009). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications, 36(4), 8013-8021. DOI : 10.1016/J.ESWA.2008.10.080
J. H. Lee. (2017). Knowledge State Analysis of the Elementary School Plane Figure unit Using the Knowledge Space Theory. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 7(5), 13-31. DOI : 10.14257/AJMAHS.2017.05.08
P. Gao. (2014). Using Personalized Education to Take the Place of Standardized Education. Journal of Education and Training Studies, 2(2), 44-47. DOI : 10.11114/JETS.v2i2.269
Chih-Ming Chen. (2008). Intelligent web-based learning system with personalized learning path guidance. Computers & Education, 51(2), 787-814. DOI : 10.1016/J.COMPEDU.2007.08.004
M. A. Chatti & A. Muslim. (2019). The PERLA Framework: Blending Personalization and Learning Analytics. International Review of Research in Open and Distributed Learning, 20(1), 243-261. DOI : 10.19173/IRRODL.v20i1.3936
A. Ramachandran & B. Scassellati. (2014). Adapting Difficulty Levels in Personalized Robot-Child Tutoring Interactions. AAAI Conference on Artificial Intelligence. (pp. 56-59). USA: AAAI.
A. Jones & G. Castellano. (2018). Adaptive Robotic Tutors that Support Self-Regulated Learning: A Longer-Term Investigation with Primary School Children, International Journal of Social Robotics, 10(3), 357-370. DOI : 10.1007/s12369-017-0458-z
F. Essalmi, L. J. E. Ayed, M. Jemni, S. Graf & Kinshuk. (2014). Generalized metrics for the analysis of E-learning personalization strategies. Computers in Human Behavior, 48(1), 310-322. DOI : 10.1016/J.CHB.2014.12.050
K. Kim & H. Shin (2016). Student-oriented Multi-dimensional Analysis System using Educational Profiling. Journal of Digital Convergence, 9(10), 263-270. DOI : 10.14400/JDC.2016.14.6.263
R. Reber, E. A. Canning & J. M. Harackiewicz. (2018). Personlized Education to Increase Interest. Current Directions in Psychological Science, 27(7), 449-454. DOI : 10.1177/0963721418793140
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