Smartness of Learning Ecosystems and its Bottom-up Emergence in six European Campuses

被引:0
作者
Giovannella, Carlo [1 ,2 ]
Andone, Diana [3 ]
Dascalu, Mihai [4 ]
Popescu, Elvira [5 ]
Rehm, Matthias [6 ]
Roccasalva, Giuseppe [7 ]
机构
[1] Univ Roma Tor Vergata, ISIM Garage, Dept Hist Cultural Heritage Educ & Soc, Rome, Italy
[2] Consorzio Roma Ric, Creat Ind, Rome, Italy
[3] Politehn Univ Timisoara, Timisoara, Romania
[4] Univ Politehn Bucuresti, Fac Automat Control & Comp, Bucharest, Romania
[5] Univ Craiova, Comp & Informat Technol Dept, Craiova, Romania
[6] Aalborg Univ, Dept Architecture Design & Media Technol, Aalborg, Denmark
[7] Politecn Torino, Dept Architecture & Design, I-10129 Turin, Italy
关键词
Learning Ecosystems; University ranking; Benchmarking; Smartness; Maslow's Pyramid; Flow; Principal Component Analysis;
D O I
暂无
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Each year a considerable amount of money is spent in the production of several national and international University rankings that may deeply influence the students' enrollment. However, all such rankings are based almost exclusively on numerical indicators weakly related to the quality of the learning process and do not consider the perceptions of the "end users", i.e. the learners. Recently, as part of the activity promoted by the ASLERD (Association for Smart Learning Ecosystems and Regional Development), we have developed an alternative approach to benchmark learning ecosystems. Such novel approach is based on: a) the detection of the degree of satisfaction related to the levels of the Maslow's Pyramid of needs, and b) the detection of indicators related with the achievement of the state of "flow" by the actors involved in the learning processes. In this paper we report on the first implementation of such benchmarking approach that involved six European Campuses and more than 700 students. The critical analysis of the outcomes allowed us to identify: a) the set of the most relevant indicators; b) a "smartness" axis in the plan of the first two principal components derived by applying a Principal Component Analysis (PCA) to the spaces of the selected indicators.
引用
收藏
页码:79 / 92
页数:14
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