See the published version of this preprint at EURASIP Journal on Wireless Communications and Networking.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Minghua Tan
Henan University of Animal Husbandry and Economy
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The English teaching network system is a distant teaching based on Web. This teaching method can stimulate students' interests, so that students can acquire knowledge voluntarily, and automatic test paper generation is one of the most important modules in English teaching network system. This paper first describes the automatic test problems, is a constrained multi-objective problem; then the design of genetic algorithm to improve the test paper, put forward questions based encoding method and based on the difficulty and test points of F fitness function for dynamic adjustment of the parameters in the iterative process. Finally, it is verified by experiments that the test paper made by this method satisfies users' requests for questions, contents and scores, and at the same time, it also improves the running efficiency of random optimization algorithm by 7-17 times.
Keywords
English Teaching Network System, Test Papers, Genetic Algorithm, Fitness Function, Wireless Network
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at EURASIP Journal on Wireless Communications and Networking.
Version 2
Posted 27 May, 2020
No community comments so far
Editorial decision: Accept
On 08 Jun, 2020
Reviewer #3 agreed
On 02 Jun, 2020
Review #3 received
Received 02 Jun, 2020
Review #1 received
Received 01 Jun, 2020
Review #2 received
Received 30 May, 2020
Reviewer #2 agreed
On 29 May, 2020
Reviewers invited
Invitations sent on 28 May, 2020
Reviewer #1 agreed
On 28 May, 2020
Editor assigned
On 18 May, 2020
Submission checks complete
On 17 May, 2020
Editor invited
On 17 May, 2020
No comments provided
Editorial decision: Major revision
On 08 May, 2020
Review #3 received
Received 05 May, 2020
Review #2 received
Received 02 May, 2020
Reviewer #3 agreed
On 30 Apr, 2020
Review #1 received
Received 28 Apr, 2020
Reviewers invited
Invitations sent on 21 Apr, 2020
Reviewer #1 agreed
On 21 Apr, 2020
Reviewer #2 agreed
On 21 Apr, 2020
Editor assigned
On 13 Apr, 2020
Editor invited
On 12 Apr, 2020
Submission checks complete
On 26 Mar, 2020
First submitted
On 19 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Systems and Networking
Learn more about our company.
See the published version of this preprint at EURASIP Journal on Wireless Communications and Networking.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Minghua Tan
Henan University of Animal Husbandry and Economy
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at EURASIP Journal on Wireless Communications and Networking.
Version 2
Posted 27 May, 2020
No community comments so far
Editorial decision: Accept
On 08 Jun, 2020
Reviewer #3 agreed
On 02 Jun, 2020
Review #3 received
Received 02 Jun, 2020
Review #1 received
Received 01 Jun, 2020
Review #2 received
Received 30 May, 2020
Reviewer #2 agreed
On 29 May, 2020
Reviewers invited
Invitations sent on 28 May, 2020
Reviewer #1 agreed
On 28 May, 2020
Editor assigned
On 18 May, 2020
Submission checks complete
On 17 May, 2020
Editor invited
On 17 May, 2020
No comments provided
Editorial decision: Major revision
On 08 May, 2020
Review #3 received
Received 05 May, 2020
Review #2 received
Received 02 May, 2020
Reviewer #3 agreed
On 30 Apr, 2020
Review #1 received
Received 28 Apr, 2020
Reviewers invited
Invitations sent on 21 Apr, 2020
Reviewer #1 agreed
On 21 Apr, 2020
Reviewer #2 agreed
On 21 Apr, 2020
Editor assigned
On 13 Apr, 2020
Editor invited
On 12 Apr, 2020
Submission checks complete
On 26 Mar, 2020
First submitted
On 19 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Systems and Networking
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at EURASIP Journal on Wireless Communications and Networking.
The English teaching network system is a distant teaching based on Web. This teaching method can stimulate students' interests, so that students can acquire knowledge voluntarily, and automatic test paper generation is one of the most important modules in English teaching network system. This paper first describes the automatic test problems, is a constrained multi-objective problem; then the design of genetic algorithm to improve the test paper, put forward questions based encoding method and based on the difficulty and test points of F fitness function for dynamic adjustment of the parameters in the iterative process. Finally, it is verified by experiments that the test paper made by this method satisfies users' requests for questions, contents and scores, and at the same time, it also improves the running efficiency of random optimization algorithm by 7-17 times.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Loading...