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pro vyhledávání: '"Sein Minn"'
Autor:
Sein Minn
Publikováno v:
Computers and Education: Artificial Intelligence, Vol 3, Iss , Pp 100050- (2022)
The growth of online learning, enabled by the availability on the Internet of different forms of didactic materials such as MOOCs and Intelligent Tutoring Systems (ITS), in turn, increases the relevance of personalized instructions for students in an
Externí odkaz:
https://doaj.org/article/ecf1057b703c4747a26bd3b6ff055337
Autor:
Sein, Minn, Shunkai, Fu
Publikováno v:
ICDM 2023:CXAI
Structure learning is essential for Bayesian networks (BNs) as it uncovers causal relationships, and enables knowledge discovery, predictions, inferences, and decision-making under uncertainty. Two novel algorithms, FSBN and SSBN, based on the PC alg
Externí odkaz:
http://arxiv.org/abs/2310.09222
Autor:
null Sein Minn
Publikováno v:
Computers and Education: Artificial Intelligence
Computers and Education: Artificial Intelligence, 2022, 3, ⟨10.1016/j.caeai.2022.100050⟩
Computers and Education: Artificial Intelligence, Vol 3, Iss, Pp 100050-(2022)
Computers and Education: Artificial Intelligence, 2022, 3, ⟨10.1016/j.caeai.2022.100050⟩
Computers and Education: Artificial Intelligence, Vol 3, Iss, Pp 100050-(2022)
International audience; The growth of online learning, enabled by the availability on the Internet of different forms of didactic materials such as MOOCs and Intelligent Tutoring Systems (ITS), in turn, increases the relevance of personalized instruc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ad2e4197ccd35c926cd8e4b83fc7f75
https://inria.hal.science/hal-03897560/document
https://inria.hal.science/hal-03897560/document
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence
Proceedings of the AAAI Conference on Artificial Intelligence, Feb 2022, Vancouver, Canada. pp.12810-12818, ⟨10.1609/aaai.v36i11.21560⟩
Proceedings of the AAAI Conference on Artificial Intelligence, Feb 2022, Vancouver, Canada. pp.12810-12818, ⟨10.1609/aaai.v36i11.21560⟩
Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c1e9e465a5af7d11442755edd2c7af5
http://hdl.handle.net/20.500.12210/80043
http://hdl.handle.net/20.500.12210/80043
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783030161446
PAKDD (2)
PAKDD (2)
Knowledge Tracing (KT) is the assessment of student’s knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process. KT leverages
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4756d28ae86d545033169da9b4bf7742
https://doi.org/10.1007/978-3-030-16145-3_13
https://doi.org/10.1007/978-3-030-16145-3_13
Publikováno v:
Application Research of Computers / Jisuanji Yingyong Yanjiu. May2016, Vol. 33 Issue 5, p1327-1334. 8p.
Publikováno v:
ICDM Workshops
Intelligent Tutoring Systems (ITS) are designed for providing personalized instructions to students with the needs of their skills. Assessment of student knowledge acquisition dynamically is nontrivial during her learning process with ITS. Knowledge
Publikováno v:
ICDM
In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. In this paper, we propose a novel model for knowledge tracing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf892555c92bbdefb8ebaebbe70aa5f3
http://arxiv.org/abs/1809.08713
http://arxiv.org/abs/1809.08713
Publikováno v:
Adaptive and Adaptable Learning ISBN: 9783319451527
EC-TEL
EC-TEL
There are numerous algorithms and tools to help an expert map exercises and tasks to underlying skills. The last decade has witnessed a wealth of data driven approaches aiming to refine expert-defined mappings of tasks to skill. This refinement can b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6aaefd9ce139f37cb14afc133aa40d7
https://doi.org/10.1007/978-3-319-45153-4_13
https://doi.org/10.1007/978-3-319-45153-4_13
Publikováno v:
DSAA
General Bayesian network classifier (GBNC) contains only features necessary for classification, so an ideal structure learning solution is to learn GBNC without having to learn the whole Bayesian network (BN). A local search based algorithm called LA