Learning Analytics: An Enabler for Dropout Prediction

Authors

  • Shu-Fen TSENG Department of Information Management, Yuan Ze University, Taiwan, Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taiwan Author
  • Chih-Yueh CHOU Department of Information Science & Engineering, Yuan Ze University, Taiwan, Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taiwan Author
  • Zhi-Hong CHEN Department of Information Communication, Yuan Ze University, Taiwan, Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taiwan Author
  • Po-Yao CHAO Department of Information Communication, Yuan Ze University, Taiwan, Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taiwan Author

Abstract

A key application of learning analytics is predicting students’ learning performances and risks of dropping out. Heterogeneous data were collected from selected school to yield a model for predicting student’s dropout. Results from this exploratory study conclude dropout prediction by learning analytics may provide more precise information on identifying at-risk students and factors causing them to be at risk.

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Published

2014-11-30

Conference Proceedings Volume

2014: ICCE 2014: The 22nd International Conference on Computers in Education

Section

Articles

How to Cite

Learning Analytics: An Enabler for Dropout Prediction. (2014). International Conference on Computers in Education. https://library.apsce.net/index.php/ICCE/article/view/3105