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An Empirical Analysis of Data-driven Intelligent Teaching Based on Cloud Class Platform

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DOI: 10.23977/aetp.2020.41011 | Downloads: 5 | Views: 157

Author(s)

Xinhong Liu 1, Yuan Feng 1, Chunxia Wu 1, Yaqin Lu 2, Di Gao 3

Affiliation(s)

1 Department of Mathematics and Physics, Beijing Institute of Petro-chemical Technology, Beijing102617, China
2 Department of Foreign Language, Beijing Institute of Petro-chemical Technology, Beijing102617, China
3 School of Mechanical Engineering, Beijing Institute of Petro-chemical Technology, Beijing102617, China

Corresponding Author

Xinhong Liu

ABSTRACT

At present, with the vigorous development of education platform, it is necessary to evaluate the effect of teaching process based on the data on the platform, but the evaluation method is relatively simple, lacking the data-driven evaluation based on the learning process. In order to achieve data-driven learning evaluation and improve teaching efficiency, the cloud class platform is used to collect the data of learners' learning process and the analysis and research of data visualization are carried out based on the platform. First, the students are divided into four categories by harmonic curve and cluster analysis. Second, the advantages and disadvantages of all kinds of students are pointed out by correspondence analysis. These results can guide teachers to use education data to mine the relevance between achievements and knowledge points, and help teachers to implement the talent training strategy of individualized teaching, improve teaching quality, provide timely feedback and control for students' learning, facilitate students' independent learning and improve learning effect.

KEYWORDS

data driven, education evaluation, cluster analysis, correspondence analysis, harmonic curve

CITE THIS PAPER

Xinhong Liu, Yuan Feng, Chunxia Wu, Yaqin Lu and Di Gao, An Empirical Analysis of Data-driven Intelligent Teaching Based on Cloud Class Platform. Advances in Educational Technology and Psychology (2020) 4: 68-75. DOI: http://dx.doi.org/10.23977/aetp.2020.41011.

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