A Statistical Analysis of the Learning Effectiveness in Online Engineering Courses

被引:4
|
作者
Araujo, R. T. S. [1 ]
Medeiros, F. N. S. [2 ]
Araujo, M. E. S. [3 ]
Lima, K. P. [4 ]
Araujo, N. M. S. [5 ]
Rodrigues, F. A. A. [1 ]
机构
[1] Univ Fed Ceara, Fortaleza, Ceara, Brazil
[2] Univ Fed Ceara, Programa Posgrad Engn Teleinformat, Fortaleza, Ceara, Brazil
[3] Inst Fed Ceara IFCE, Juazeiro Do Norte, Ceara, Brazil
[4] Univ Fed Lavras, Lavras, MG, Brazil
[5] Univ Estadual Ceara UECE, Fortaleza, Ceara, Brazil
关键词
Multivariate analysis; learning effectiveness; distance education; engineering education; PRINCIPAL COMPONENT ANALYSIS; VARIABLES; COMPLEX;
D O I
10.1109/TLA.2017.7854626
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Engineering education as a field of study is young, and the number of experienced researchers investigating the complex learning questions associated with becoming an engineer still sparse [44]. In this perspective, the following research question can be outlined: Do educational resources that are available in online engineering courses, such as Virtual Learning Environment, Content Instructional Design and Simulations, impact the effectiveness of learning? The main objective of this article is to statistically assess the effectiveness of e-learning courses in engineering. To fulfill the research objective, the use of the investigated learning resources was observed in a distance learning course, and an evaluation instrument was adapted, validated and applied to the students. To test the learning effectiveness, multivariate statistical methods were used to analyze the instrument, namely: Principal Component Analysis (PCA) and canonical correlation analysis. The multivariate analysis with the canonical correlation discloses how the positive contribution of the two groups arises from the PCA application: Functionality of Virtual Environment and Design Content and Simulations in learning effectiveness.
引用
收藏
页码:300 / 309
页数:10
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