Decision tree for FSSA rooms in CANDU

Shalabi, Hossam; Hadjisophocleous, George

doi:10.1016/j.nucengdes.2020.110568

Cited by 1 publication

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“…The network provides a powerful and integrated information medium, and the collection and acquisition of information resources is an important form of online learning activities. In the network environment, learners not only learn the content of online courses, but also learn auxiliary resources and resources on the Internet, web browsing and information retrieval and other behaviors (Di & Xu, 2019;Shalabi & Hadjisophocleous, 2020). Information processing is the process of understanding and absorbing information, including perception, attention, memory, comprehension and other processes.…”

Section: Online Learning Mechanismmentioning

confidence: 99%

Online Learning Behavior Feature Mining Method Based on Decision Tree

Shao¹,

Gao²,

Wang³

2022

Journal of Cases on Information Technology

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This research mainly discusses the design of online learning behavior feature mining method based on decision tree. Data collection is the real-time collection of online learning behavior data from distance learning websites. OWC (Office Web Component) technology is used to draw real-time charts on the page. Online learning students are selected as the research object, and the student's system log data and questionnaire data are selected. When combining the pre-pruning method and the post-pruning method to make decisions after the tree is pruned, the same source data is used to adjust, test and evaluate the decision tree model. The evaluation process to generate a complete decision tree is completed by the c4.5tree algorithm in C4.5, which can be named with a suffix of .names The type definition file is used to record the type of each attribute item or the range of possible values. In the study, the prediction accuracy rate of predicting learning effect based on "online learning behavior" reached more than 66%.

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Section: Online Learning Mechanismmentioning

confidence: 99%