Design of Pretreatment for Keyword-Based Search over Network Teaching Resource Database

2008 ◽  
Author(s):  
Xian Zhong
Keyword(s):  
Network Teaching ◽  
Teaching Resource

Development and Application of Information Technology in University Basketball Network Courses

2014 ◽  
Vol 1078 ◽  
pp. 345-348
Author(s):  
Ke Fei Wang
Keyword(s):  
Computer Network ◽  
Sports Injuries ◽  
Theory And Practice ◽  
Online Forums ◽  
Network Teaching ◽  
Module Design ◽  
Teaching Resource ◽  
System Maintenance ◽  
Network Platform ◽  
Resource Library

Based on the university basketball teaching theory and practice,combined with module design theory.Using literature studies,surveys,interviews,computer network design and software development,testing feedback methods designed modern colleges.basketball network teaching courses.The system includes basketball theoretical teaching resource library,basketball skills teaching resource library, basketball learning online forums, basketball sports injuries advisory system and system maintenance updates five modules. Expounded the issue of the system design, key technologies, modules and functional design,running instances and so on. Research and development of the system provided a good network platform for the college basketball training,so make the existing basketball teaching and training network, made the quality of teaching to improve and enhance.

scholarly journals Image Network Teaching Resource Retrieval Algorithm Based on Deep Hash Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Guotao Zhao ◽  
Jie Ding
Keyword(s):  
Retrieval Algorithm ◽  
Data Detection ◽  
Analysis Method ◽  
Teaching Resources ◽  
Moment Invariant ◽  
Network Teaching ◽  
Detection Model ◽  
Teaching Resource ◽  
Hash Algorithm ◽  
Image Network

In order to improve the retrieval ability of multiview attribute coded image network teaching resources, a retrieval algorithm of image network teaching resources based on depth hash algorithm is proposed. The pixel big data detection model of the multiview attribute coding image network teaching resources is constructed, the pixel information collected by the multiview attribute coding image network teaching resources is reconstructed, the fuzzy information feature components of the multiview attribute coding image are extracted, and the edge contour distribution image is combined. The distributed fusion result of the edge contour of the view image of the network teaching resources realizes the construction of the view feature parameter set. The gray moment invariant feature analysis method is used to realize information coding, the depth hash algorithm is used to realize the retrieval of multiview attribute coded image network teaching resources, and the information recombination is realized according to the hash coding result of multiview attribute coded image network teaching resources, thus improving the fusion. The simulation results show that this method has higher precision, better retrieval precision, and higher level of resource fusion for multiview coded image network teaching resource retrieval.

Study on Knowledge Base Construction for the Network Teaching Resource Based on Semantic Web

2009 ◽  
Vol 16-19 ◽  
pp. 758-763
Author(s):  
Jian Hou Gan ◽  
Ling Yun Yuan
Keyword(s):  
Semantic Web ◽  
System Design ◽  
Knowledge Base ◽  
Teaching System ◽  
Network Teaching ◽  
Teaching Resource ◽  
Knowledge Base Construction ◽  
Active Research ◽  
Regular Relation ◽  
Resource Knowledge

Intelligent teaching system design has become an active research topic in education research community. How to manage the network teaching resource efficiently will have a direct impact on the teaching process and quality in the network teaching system. The semantic web can be a very promising platform for knowledge management systems in the intelligent teaching system design. In this paper, we have presented a framework for the network teaching resource knowledge base (NTRKB) based on semantic web, which includes network teaching resource knowledge representation, regular relation definition based on OWL, knowledge base modeling and construction based on RDF. The NTRKB can achieve knowledge sharing and intelligent retrieval for the network teaching resource, and these components will greatly benefit the development of the proposed intelligent teaching system.

scholarly journals Research on Teaching Resource Recommendation Algorithm Based on Deep Learning and Cognitive Diagnosis

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Fei Zhou
Keyword(s):  
Joint Probability ◽  
Matrix Decomposition ◽  
Cognitive Diagnosis ◽  
Learning Needs ◽  
Test Question ◽  
Recommendation Algorithm ◽  
Teaching Resources ◽  
Network Teaching ◽  
Teaching Resource ◽  
History Of

With the increasing abundance of network teaching resources, the recommendation technology based on network is becoming more and more mature. There are differences in the effect of recommendation, which leads to great differences in the effect of recommendation algorithms for teaching resources. The existing teaching resource recommendation algorithm either takes insufficient consideration of the students’ personality characteristics, cannot well distinguish the students’ users through the students’ personality, and pushes the same teaching resources or considers the student user personality not sufficient and cannot well meet the individualized learning needs of students. Therefore, in view of the above problem, combining TDINA model by the user for the students to build cognitive diagnosis model, we put forward a model based on convolution (CUPMF) joint probability matrix decomposition method of teaching resources to recommend the method combined with the history of the students answer, cognitive ability, knowledge to master the situation, and forgetting effect factors. At the same time, CNN is used to deeply excavate the test question resources in the teaching resources, and the nonlinear transformation of the test question resources output by CNN is carried out to integrate them into the joint probability matrix decomposition model to predict students’ performance on the resources. Finally, the students’ knowledge mastery matrix obtained by TDINA model is combined to recommend corresponding teaching resources to students, so as to improve learning efficiency and help students improve their performance.

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