Using Computational Methods to Analyze Educational Data

被引:1
|
作者
Vieira, Camilo [1 ]
Magana, Alejandra [2 ]
Boutin, Mireille [3 ]
机构
[1] Univ None, Dept Educ, Barranquilla, Colombia
[2] Purdue Univ, Comp & Informat Technol, W Lafayette, IN 47907 USA
[3] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
基金
美国国家科学基金会;
关键词
Pattern recognition; clustering; computation; research methods; educational research; information visualization;
D O I
10.1109/fie43999.2019.9028599
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
This paper proposes a special session on the use of computational methods for analyzing educational data. Computation has permeated all disciplines because it provides unique opportunities to represent knowledge and understand complex phenomena. In education, disciplines such as learning analytics and educational data mining have emerged to better understand educational phenomena. This special session will discuss three different approaches to use computational methods to analyze qualitative educational data. After the discussion, the participants will be able to implement these methods using R programming, while reflecting on how they can use these methods in their own context.
引用
收藏
页数:4
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