Author(s)

Sa Chen ¹, Xiankun Lin ¹

Affiliation(s)

¹ University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Sa Chen

ABSTRACT

Decision tree algorithms in Educational Data Mining (EDM) emerges as a powerful method for student classification according to their learning station. This article emphasizes the necessity to classify students due to the changing scenario of China's higher education system, which has shifted from elitist to popularization, and lead to university students' differences in learning abilities. For the specialty characteristics of educational data, this study adopts decision tree algorithm based on ID3 to classify students. This article presents an analysis of the application of decision tree algorithm in the course "Hydraulic and Pneumatic Technology Course Design," which is a mandatory course for Mechanical Design Manufacture and Automation Major. This study identifies five splitting features that affect the students' ability to achieve success in this practical course, and build the decision tree model. The learning samples for this algorithm mode are collected from Bachelor students of the University of Shanghai for Science and Technology (USST).  According to the categorization results teachers make informed decisions based on the insights provided by the algorithm to improve the learning experience and academic performance of students. Similarly categorization results also provide personalized guidance for students, which is beneficial in ensuring their success and ultimately improving overall educational outcomes.

KEYWORDS

Decision Tree, Student Classification, Educational Data Mining, University Teaching

CITE THIS PAPER

Sa Chen, Xiankun Lin, Application of Decision Tree Algorithm in Educational Data Mining. Curriculum and Teaching Methodology (2023) Vol. 6: 120-127. DOI: http://dx.doi.org/10.23977/curtm.2023.060818.

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Sponsors, Associates, and Links

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• Advances in Educational Technology and Psychology
  
  
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• Applied & Educational Psychology
  
  
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