文章基本信息

标题：The Use of Genetic Algorithm, Multikernel Learning, and Least-Squares Support Vector Machine for Evaluating Quality of Teaching
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作者：Yingying Yi ; Hao Zhang ; Hanen Karamti 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2022
卷号：2022
DOI：10.1155/2022/4588643
语种：English
出版社：Hindawi Publishing Corporation
摘要：The educational data mining (EDM) methods are increasingly diversified. In this research, a hybrid method of multikernel learning (MKL), least-squares support vector machine (LSSVM), and genetic algorithm (GA) is employed to evaluate teaching quality through nine indicators; the reliability of our proposed method is evaluated by confidence interval and prediction interval. First, English teaching quality samples occurring from three age groups at Guizhou Normal University are collected. Next, an intelligent method MK-LSSVM is proposed. Finally, the test sets are regression by the proposed model, and regression results are evaluated by confidence interval, prediction interval, and several error calculation methods; we also develop an ablation experiment for our proposed model. The experiment indicates that the MKL-LSSVM-GA outperforms other benchmark methods at three age-group levels. Additionally, at all three age-group levels, the experiment indicates that three indicators are crucial for the evaluation of teaching quality. Therefore, the proposed model in this paper can evaluate the English teaching quality effectively.