Learning analytics and open, flexible, and distance learning

被引:16
|
作者
Siemens, George [1 ,2 ]
机构
[1] Univ South Australia, Ctr Change & Complex Learning, Adelaide, SA, Australia
[2] Univ Texas Arlington, Psychol, Arlington, TX 76019 USA
关键词
learning analytics; open; flexible and distance learning; data; ethics; SELF-ESTEEM; BIG; 5;
D O I
10.1080/01587919.2019.1656153
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
This article serves as a reflection on the papers accepted for this special issue. Although 2011 saw the first learning analytics conference, the field draws from a long history of research in associated disciplines such as psychology, statistics, education and computer science. However, the novelty of learning analytics research stems from the access and analysis of large, yet granular, data sources that are generated from student interactions in online activities. The vast array and volume of accessible learning data have promulgated new approaches to understanding and measuring learning. As researchers pursue such algorithmically generated insights, there are parallel challenges to individual ethics and privacy and a broader consideration of the impact of an increased quantification of education. This special issue on the role of learning analytics in open, flexible, and distance learning (OFDL) environments aims to bring alternate perspectives to bear on how such analyses are improving learning while still addressing social and cultural challenges.
引用
收藏
页码:414 / 418
页数:5
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