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Predicting academic performance using tree-based machine learning models: A case study of bachelor students in an engineering department in China

Abstract

Educational data mining (DEM) provides valuable educational information by applying data mining tools and techniques to analyze data at educational institutions. In this paper, tree-based machine learning algorithms are used to predict students’ overall academic performance in their bachelor’s program. The transcript data of the students in the same department in a Chinese university were collected. All the courses in the bachelor’s program were then divided into six typical categories, and the mean GPAs of each category were taken as primary input features for prediction. Three tree-based machine learning models were established, i.e. decision tree (DT), Gradient boosting decision tree (GBDT) and random forest (RF). Results show that we can successfully identify more than 80% of the students at low-performance risk using the RF model at the end of the second semester, which is meaningful because the global quality of teaching and learning of the department can be improved by taking targeted measures in time according to the machine learning model. Feature importance and the structure of decision tree were also analyzed to extract knowledge that is valuable for both students and teachers. The results of this case study can be used as a reference for other engineering departments in China.

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Acknowledgments

This work is funded by the 2021 Project of Higher Education Teaching Quality and Teaching Reform sponsored by the Department of Education of Guangdong Province (Grant No. 2021-8.2).

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Correspondence to Wei Zhang.

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Zhang, W., Wang, Y. & Wang, S. Predicting academic performance using tree-based machine learning models: A case study of bachelor students in an engineering department in China. Educ Inf Technol (2022). https://doi.org/10.1007/s10639-022-11170-w

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  • DOI: https://doi.org/10.1007/s10639-022-11170-w

Keywords

  • Educational data mining
  • Quality of teaching and learning
  • Classification
  • Decision tree
  • Engineering education
  • Department administration