期刊名称：International Journal of Emerging Technologies in Learning (iJET)
印刷版ISSN：1863-0383
出版年度：2018
卷号：13
期号：10
页码：146-157
语种：English
出版社：Kassel University Press
摘要：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.
关键词：decision tree algorithm;statistical analysis;teaching quality evaluation;teaching direction prediction
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