Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm ARTICLE
Sujuan Jia, Department of Teaching Affairs, Hebei University of Science and Technology ; Yajing Pang, Department of Development Planning, Hebei University of Science and Technology
iJET Volume 13, Number 10, ISSN 1863-0383 Publisher: International Association of Online Engineering, Kassel, Germany
Vast data in the higher education system are used to analyse and evaluate the teaching quality, so that the key factors that affect the quality of teaching can be predicted. Besides, the learner’s personalized behaviour can also become the data source for teaching result prediction. This paper proposes a decision tree model by taking the teaching quality data and the statistical analysis results of the learn-er’s personalized behaviour as inputs. This model was based on the improved C4.5 decision tree algorithm, which used the FAYYAD boundary point decision theorem for effectively reducing the computation time to the most threshold. In this algorithm, the iterative analysis mechanism was introduced in combination with the data change of the learner’s personalized behaviour, so as to dynamically adjust the final teaching evaluation result. Finally, according to the actual statisti-cal data of one academic year, the teaching quality evaluation was effectively completed and the direction of future teaching prediction was proposed.
Jia, S. & Pang, Y. (2018). Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm. International Journal of Emerging Technologies in Learning (iJET), 13(10), 146-157. Kassel, Germany: International Association of Online Engineering. Retrieved November 18, 2018 from https://www.learntechlib.org/p/185274/.