NDLI: Analytic Scoring of Landscape CAD Course Based on BP Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Zhang Yi-chuan Qiao Li-fang Zhang Li-lei Chen Teng
Copyright Year	2009
Abstract	Course Scoring is the major criteria for measuring the teaching effect and a basis for improving the education quality. Analytic scoring of landscape CAD (Computer-Aided Design) course is influenced by various factors. Relationships among these factors are complex, some are nonlinear, even some are random and fuzzy. It is difficult to explain their internal relationships with traditional method. This research combines Back-Propagation neural network and DPS software to establish a three-layer BP neural network model, which took 60 examination papers of landscape CAD course as samples and made predictions on the score in accordance with five factors, including landscape design standard, landscape design innovation, computer cartography standard, drawing effect and workload. The results show that BP neural network model has strong nonlinear approximation ability, could truly reflects the nonlinear relationships between global score of landscape CAD course and main controlling factors of analytic scoring, with small error between predicted values and the measured values, relative error lower than 5%. In the future, when analytic scoring of the landscape CAD course obtains from the teachers, the global scoring can be calculated by BPNN model automatically. This method showed wide application prospect to the courses need analytic scoring.
Starting Page	13
Ending Page	16
File Size	251073
Page Count	4
File Format	PDF
ISBN	9780769536095
DOI	10.1109/ICETC.2009.57
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-17
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Technological innovation landscape CAD course Design automation analytic scoring Predictive models BP Neural Network Neural networks Automatic control Computer errors Software standards Computer networks Error correction Computer science education
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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