| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
The LMS has become popular mainly among institutions of higher education and the learning history data has been accumulated, which is becoming big data. Analyzing the information is increasingly expected to be utilized for the improvement of learning activities and the prediction of future abilities. This study aimed to construct a learning support system suitable to individuals by extracting learning tendencies of individuals from the information accumulated in e-learning.
To extract the learning tendencies, a method that categorizes the characteristics systematically based on the k-means method was developed. To predict the learning results, a method utilizing CR-SOM that can consider and visualize the elements was developed. As the result of adjusting to actual data, data of the same elements tended to concentrate closely more than SOM, so the result that leads to a prediction with high identification rate was obtained.
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