基于主动学习支持向量机的辅助教学质量评估模型
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摘要
传统的高校教学质量评估方式在精度和实施效率方面已经无法满足信息化和现代化的教学模式。因此,针对高校课堂教学质量的评估问题,提出一种基于主动学习支持向量机的辅助教学质量评估模型。综合考虑多方面的实际情况,构建课堂教学质量的评估指标体系。采用主动学习支持向量机建立课堂教学质量评估模型。对收集到的某高校教学质量相关数据集进行实验,并分析其结果。实验结果表明,相比其他评估模型,提出的评估模型在精度和效率上具有一定优势,能够获得较好的高校教学质量评估结果。
The traditional methods of teaching quality evaluation in colleges and universities can′t meet the teaching mode of informationization and modernization due to the low precision and implementation efficiency. Therefore,an assisted teaching quality evaluation model based on active learning support vector machine is proposed for the classroom teaching quality evaluation in colleges and universities. The evaluation index system of classroom teaching quality is constructed by considering the actual situation in many aspects. The active learning support vector machine is used to establish the classroom teaching quality evaluation model. The experiment is carried out for the collected teaching quality relevant dataset of a certain university,and its results are analyzed. The experimental results show that,in comparison with other evaluation models,the proposed evaluation model has higher accuracy and efficiency,and can obtain better evaluation results of teaching quality in colleges and universities.
The traditional methods of teaching quality evaluation in colleges and universities can′t meet the teaching mode of informationization and modernization due to the low precision and implementation efficiency. Therefore,an assisted teaching quality evaluation model based on active learning support vector machine is proposed for the classroom teaching quality evaluation in colleges and universities. The evaluation index system of classroom teaching quality is constructed by considering the actual situation in many aspects. The active learning support vector machine is used to establish the classroom teaching quality evaluation model. The experiment is carried out for the collected teaching quality relevant dataset of a certain university,and its results are analyzed. The experimental results show that,in comparison with other evaluation models,the proposed evaluation model has higher accuracy and efficiency,and can obtain better evaluation results of teaching quality in colleges and universities.
引文
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[2] CHEN C Y,CHEN P C,CHEN P Y. Teaching quality in higher education:an introductory review on a process-oriented teaching-quality model[J]. Total quality management & business excel-lence,2014,25(1/2):36-56.
[3] LU G. Unmasking the teaching quality of higher education:stu-dents′course experience and approaches to learning in China[J]. Assessment & evaluation in higher education,2014,39(8):949-970.
[4] COHEN J,BROWN M. Teaching quality across school settings[J]. New educator,2016,12(2):191-218.
[5]刘智萍.证据理论和支持向量机相融合的高校教学质量评价[J].现代电子技术,2017,40(17):175-178.LIU Zhiping. Evaluation of teaching quality in colleges and uni-versities based on the combination of evidence theory and sup-port vector machine[J]. Modern electronics technique,2017,40(17):175-178.
[6]董国玉,祁迎春.层次分析法在高校课堂教学质量评估中的应用研究[J].中国成人教育,2017(9):53-56.DONG Guoyu,QI Yingchun. Application of analytic hierarchy process in the evaluation of classroom teaching quality in col-leges and universities[J]. China adult education,2017(9):53-56.
[7]郑永,陈艳.基于BP神经网络的高校教师教学质量评估模型[J].重庆理工大学学报,2015,29(1):85-90.ZHENG Yong,CHEN Yan. Evaluation model of college teachers′teaching quality based on BP neural network[J]. Journal of Chongqing University of Technology,2015,29(1):85-90.
[8] MARATHE A R,LAWHERN V J,WU D,et al. Improved neural signal classification in a rapid serial visual presentation task using active learning[J]. IEEE transactions on neural sys-tems & rehabilitation engineering,2016,24(3):333-343.
[9] JINDAL A,DUA A,KAUR K,et al. Decision tree and SVM-based data analytics for theft detection in smart grid[J]. IEEE transactions on industrial informatics,2016,12(3):1005-1016.