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Implementation of Neural Networks in Predicting the Understanding Level of Students Subject
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- 발행기관
- 보안공학연구지원센터(IJSEIA) 바로가기
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- 간행물
- International Journal of Software Engineering and Its Applications SCOPUS 바로가기
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- 통권
- Vol.10 No.10 (2016.10)바로가기
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- 페이지
- pp.189-204
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A288552
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- This paper implements artificial neuralnetworkin predictingthe understanding level ofstudent’scourse. By implementing artificial neural network based on backpropagation algorithm, an institution can give a fair decision in prediction level of students' understanding of particular course / subject. This method was chosen because it is able to determine the level of students' understanding of the subject based on input from questionnaires given. The study was conducted into two ways, namely training and testing. Data will be divided into two parts, the first data for the training process and the second reading data of the testing process. The training process aims to identify or search for goals that are expected to use a lot of patterns. Thus, it will be able to produce the best pattern to train the data. After reaching the goal of training which is based on the best pattern, then it will be tested with new data to seeat the accuracy of the target data using Matlab 6.1 software. The results show that it can accelerate the process of prediction of students' understanding. By using architectural models 6-50-1 as the best model, some architectural models are tested and the result of prediction is reach to 87.75%. In other word, this model is good enough to make predictions on the level of students' understanding of the subject.
목차
Abstract
1. Introduction
2. Rudimentary
2.1. Artificial Intelegence
2.2. Artificial Neural Networks (NN)
2.3. Architecture of Backpropogation
2.4. Backpropagation Neural Network
2.5. Evaluating the Performance of the Models
3. Experiment Design
3.1. Data Collection
3.2. Data Processing
3.3. Manual Design of Architectural Patterns
4. Results and Discussion
4.1. The Best Pattern Determination
4.2. Students Understanding Level Predictions of the Course
5. Conclusion
References
키워드
Artificial neural network Backpropogation Level Comprehension Student Subject저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJSEIA) [Science & Engineering Research Support Center, Republic of Korea(IJSEIA)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Software Engineering and Its Applications
- 간기
- 월간
- pISSN
- 1738-9984
- 수록기간
- 2008~2016
- 등재여부
- SCOPUS
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Software Engineering and Its Applications Vol.10 No.10
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