淡江大學機構典藏:Item 987654321/59938
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    題名: Mining Test Results to Personalise and Refine Web-Based Courses
    作者: Hsu, Hui-Huang
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: distance education;web mining;personalised courses;course refinement;TRF;test results feedback;feedback models;e-learning;electronic learning;online learning;appropriate learning content;distance learning
    日期: 2010-07
    上傳時間: 2011-10-05 22:28:46 (UTC+8)
    出版者: Olney: Inderscience Enterprises
    摘要: Providing appropriate learning content to each student is a key to the success of a web-based distance learning system. Student test results can be an important feedback for the instructor to re-evaluate the course content. A Test Result Feedback (TRF) model that analyses the relationship between student learning time and the corresponding test result is developed. The model can give the instructor crucial information for course content refinement. It can also suggest the student with a personalised remedial course or appropriate advanced courses for further study. All these can be done automatically without interfering with the student's learning and/or increasing the instructor's working load. In our design, all web courses are dynamically assembled with selected course units.
    關聯: International Journal of Applied Systemic Studies 3(2), pp.183-191
    DOI: 10.1504/IJASS.2010.034108
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

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