摘要
分析高校主观教学评价对于提升高校课堂教学质量具有重要意义。该文构建了基于5个纬度的高校主观教学评价情感分析模型,对主观教学评价反馈文本进行分析;在此基础上,利用机器学习中的K近邻算法(K-Nearest Neighbor,KNN),提出了可实现“褒、贬、中立”三分类的主观教学评价分类方法。实验结果表明:该文提出的基于文本分析法的高校主观教学评价方法可以获得较高的教学评价分类准确度。
Analyzing teaching subjective evaluation has important guiding significance for improving teaching quality.A five-latitude university teaching subjective evaluation sentiment analysis model is constructed to analyze the teaching subjective evaluation feedback text.And on the basis of this model,a K-nearest neighbor(KNN)based classification algorithm is proposed to realize the three classifications of“commendation,derogation,and neutrality”.The experimental results show that the subjective evaluation method of university teaching based on text analysis proposed in this paper can obtain a high classification accuracy.
作者
丁学君
甘甜
田勇
DING Xuejun;GAN Tian;TIAN Yong(School of Management Science and Engineering,Dongbei University of Finance and Economics,Dalian Liaoning,116025,China;Business School,University of Nottingham Ningbo China,Ningbo Zhejiang,315100,China;School of Physics and Electronic Technology,Liaoning Normal University,Dalian Liaoning,116029,China)
出处
《创新创业理论研究与实践》
2024年第9期10-16,共7页
The Theory and Practice of Innovation and Enterpreneurship
基金
国家自然科学基金项目“多重社交网络环境下突发事件谣言治理:耦合传播、协同演化及最优控制”(72374040)
国家自然科学基金项目“多源混合干扰环境下基于无线被动感知的运动人体目标多维信息识别研究”(62076114)
辽宁省教育科学“十三五”规划项目“融媒体环境下高校舆情治理与课程思政协同创新机制研究”(JG20DB142)
辽宁师范大学研究生教育教学改革研究资助项目“基于PBL教学法的研究生课程柔性化教学改革的探索与实践”(YJSJG202308)
关键词
教学评价
文本分析
情感分析
语义规则
机器学习
K近邻算法
Teaching evaluation
Text analysis
Sentiment analysis
Semantic rules
Machine learning
K-Nearest neighbor algorithm
