A decision tree approach to predictive modeling of student performance in engineering dynamics

• Autores: N. Fang, J. Lu
• Localización: The International journal of engineering education, ISSN-e 0949-149X, Vol. 26, no. 1, 2010, págs. 87-95
• Idioma: inglés
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• Resumen

  • A decision tree model has been developed to predict student performance in Engineering Dynamics based on 750 data records collected from 125 students in two semesters. The predictor variables include a student's cumulative GPA and scores in four prerequisite courses: Engineering Statics, Calculus I, Calculus II, and Physics. The model generates nine decision rules and shows that a student's performance in Statics and cumulative GPA play the two most significant roles in governing the student's performance in Dynamics. The prediction accuracy of the model is more than 80%, which is at least 14% higher than that of the traditional multivariate regression model.