A Comparison of Learning Analytics Frameworks: a Systematic Review

被引:17
作者
Khalil, Mohammad [1 ]
Prinsloo, Paul [2 ]
Slade, Sharon [3 ]
机构
[1] Univ Bergen, Ctr Sci Learning & Technol SLATE, Bergen, Norway
[2] Univ South Africa UNISA, Pretoria, South Africa
[3] Earth Trust, London, England
来源
LAK22 CONFERENCE PROCEEDINGS: THE TWELFTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE | 2022年
关键词
Learning analytics; framework; systematic review; literature review; comparison; HIGHER-EDUCATION; MODEL; DESIGN;
D O I
10.1145/3506860.3506878
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
While learning analytics frameworks precede the official launch of learning analytics in 2011, there has been a proliferation of learning analytics frameworks since. This systematic review of learning analytics frameworks between 2011 and 2021 in three databases resulted in an initial corpus of 268 articles and conference proceeding papers based on the occurrence of "learning analytics" and "framework" in titles, keywords and abstracts. The final corpus of 46 frameworks were analysed using a coding scheme derived from purposefully selected learning analytics frameworks. The results found that learning analytics frameworks share a number of elements and characteristics such as source, development and application focus, a form of representation, data sources and types, focus and context. Less than half of the frameworks consider student data privacy and ethics. Finally, while design and process elements of these frameworks may be transferable and scalable to other contexts, users in different contexts will be best-placed to determine their transferability/scalability.
引用
收藏
页码:152 / 163
页数:12
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