Pipeline for Expediting Learning Analytics and Student Support from Data in Social Learning

被引:4
|
作者
Jo, Yohan [1 ]
Tomar, Gaurav [1 ]
Ferschke, Oliver [1 ]
Rose, Carolyn Penstein [1 ]
Gasevic, Dragan [2 ,3 ]
机构
[1] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA
[2] Univ Edinburgh, Sch Educ, Edinburgh, Midlothian, Scotland
[3] Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland
来源
LAK '16 CONFERENCE PROCEEDINGS: THE SIXTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE, | 2016年
关键词
Learning Analytics; Social Learning;
D O I
10.1145/2883851.2883912
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
An important research problem in learning analytics is to expedite the cycle of data leading to the analysis of student progress and the improvement of student support. For this goal in the context of social learning, we propose a pipeline that includes data infrastructure, learning analytics, and intervention, along with computational models for individual components. Next, we describe an example of applying this pipeline to real data in a case study, whose goal is to investigate the positive effects that goal-setting students have on their peers, which suggests ways in which we might foster these social benefits through intervention.
引用
收藏
页码:542 / 543
页数:2
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