| Presentation | 2015-03-14 Imputation for Educational Data Based on Data Feature Selection Nuo Zhang, Hiroaki Kimura, Masanori Takagi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Using collected data from an e-Learning system, improvement of instructions and student's performance can be expected. However, missing value always existed in the collected data in real world makes the low accuracy of the data analysis. In this paper, we propose a missing value imputation method using cluster analysis with e-Learning log data and questionnaires answered by students who have taken the e-Learning courses. The simulation results show the performance of the proposed method. For example, we found a reduction in the incorrect prediction result for those who were not passing the real examination using our proposal. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | missing value estimation / educational data mining / data mining |
| Paper # | ET2014-101 |
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| Conference Information | |
| Committee | ET |
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| Conference Date | 2015/3/7(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Educational Technology (ET) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Imputation for Educational Data Based on Data Feature Selection |
| Sub Title (in English) | |
| Keyword(1) | missing value estimation |
| Keyword(2) | educational data mining |
| Keyword(3) | data mining |
| 1st Author's Name | Nuo Zhang |
| 1st Author's Affiliation | KDDI R&D Laboratories() |
| 2nd Author's Name | Hiroaki Kimura |
| 2nd Author's Affiliation | KDDI R&D Laboratories |
| 3rd Author's Name | Masanori Takagi |
| 3rd Author's Affiliation | Iwate Prefectural University |
| Date | 2015-03-14 |
| Paper # | ET2014-101 |
| Volume (vol) | vol.114 |
| Number (no) | 513 |
| Page | pp.pp.- |
| #Pages | 5 |
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