On-line Data-analysis of e-Learning Response Time using Gamma Distribution

• UENO Maomi

  Faculty of Engineering, Nagaoka University of Thechnology

• NAGAOKA Keizo

  Graduate School of School of Human Sciences:School of Human Sciences, Waseda University

抄録

This paper proposes a new automatic characteristics analysis method of e-learning contents using the response curve of e-Learning time data in the learning historical data-base. Many researches concerned with the mathematical model of the response time data have been studied, but they have a disadvantage that it is very difficult to interpret the parameters and estimate the parameters. The proposed model of e-learning time data in this paper is derived from maximizing the Entropy as same as Nagaoka, Wu (1989), but the unique feature of the paper is to derive the model with the following two parameters by employing the maximizing Entropy method with some restrictions to make the parameters interpretation more easily. The two parameters α and β in the model are respectively interpreted as follows: The parameter α means "Complexity of the content (which means the numbers of simple understanding processes to understand or solve the content)" and the parameter β means "Expected time of the simple understanding process in the content". That is, this means that the average of the learning time for a content is divided into the two parameters α and β. In the other words, the average of the learning time for a content is equivalent to the product of the two parameters α and β. Using these properties of the parameters, this paper proposes a new content evaluation method using α-β plain, which is called" α-β chart". Furthermore, the authors developed a LMS (Learning Management System) which it is inserted into, and show the effectiveness of the system by practical use.

収録刊行物

• Educational technology research

  Educational technology research 29 (1-2), 65-73, 2006

  日本教育工学会

キーワード

• eLearning
• WBT
• LMS
• Data Analysis
• Learning Time
• Response Curve

詳細情報

• CRID: 1390001205219056000
• NII論文ID: 110006793693
• NII書誌ID: AA00174437
• DOI: 10.15077/etr.kj00004963301
• ISSN: 21897751
• ISSN: 03877434
• 本文言語コード: en
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可