Towards Learning Analytics Metamodels in a Context of Publishing Chains

被引:1
作者
Canellas, Camila Morals [1 ,2 ]
Bouchet, Francois [1 ]
Arribe, Thibaut [2 ]
Luengo, Vanda [1 ]
机构
[1] Sorbonne Univ, CNRS, LIP6, F-75005 Paris, France
[2] Soc Kelis, Lyon, France
来源
CSEDU: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED EDUCATION - VOL 2 | 2021年
关键词
Learning Analytics; Models; Model-driven Engineering; Publishing Chains;
D O I
10.5220/0010402900450054
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In a context of pedagogical resource production via publishing chains that are based on an model-driven engineering approach, we consider the proposition of a learning analytics implementation. We argue that, by using the same approach to carry out such an implementation versus a classical one, a series of benefits could be assessed, whether they are related to the fact that it is using this specific context, methodological approach or both. Perhaps one of the most particular benefits is the detailed knowledge of the semantics and structure of any document produced, that could therefore be automatically added to the traces/analysis. Other potential improvements discussed are: separation of content and form, interoperability, compliance with data privacy, maintainability, performance, multi format, customization and reproducibility.
引用
收藏
页码:45 / 54
页数:10
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