A Neural Network Model for Predicting the Error Rates of Students for a Learning Problem
| Autor: | Kazuhiro Shin-Ike, Hiroshi Nakamine, Nobuo Sannomiya, Hitoshi Iima |
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| Rok vydání: | 2004 |
| Předmět: |
Artificial neural network
Learning problem Computer science business.industry media_common.quotation_subject Machine learning computer.software_genre Generalization error Learning effect ComputingMilieux_COMPUTERSANDEDUCATION Aptitude Sensitivity (control systems) Artificial intelligence business computer media_common |
| Zdroj: | Transactions of the Institute of Systems, Control and Information Engineers. 17:297-304 |
| ISSN: | 2185-811X 1342-5668 |
| Popis: | In general it is impossible to know the learning effect of students before teaching them. Therefore, teachers have to predict it in order to perform actual teaching effectively and efficiently. In this paper, we propose a method to predict the error rates of the students for a learning problem and analyze how to teach students effectively through the prediction results. For this purpose, a multi-layer neural network (MNN) model is used. In this model, the input variables are five aptitude abilities of a student and the output variables are three error rates. It is confirmed that the prediction values obtained by using this MNN are reasonable as compared with the experimental results. Moreover, from the sensitivity analysis, the aptitude abilities to reduce the error rates are identified. This result makes it possible to predict the error rates of a learning problem in advance. By using these results, teachers can instruct students more effectively and efficiently. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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