基于在线学习行为的学习效果预测,这篇学习效果预测论文范文为免费优秀学术论文范文,可用于相关写作参考。
学习效果预测论文参考文献：
摘 要：隨着互联网不断地发展,越来越多的人选择通过MOOC平台学习知识.但是,在网络上,老师很难知道学习者的学习情况,不利于对教学资源配置和指导教学过程进行调整.本研究提出建立学生学习动态模型,对学生学习情况进行分析并及时反馈学习结果.本研究先利用最近邻算法中的IBK算法,对学生行为数据的异常数据进行排除,运用等频装箱法将学生的学习成绩和学习行为数据进行处理.利用GBDT模型、SVM模型建立学生学习成绩预测模型；最后利用随机森林模型对数据进行训练,建立学生学习成绩影响因素模型,并对模型进行分析.通过学生学习成绩影响因素模型的分析,找出影响学生学习成绩的主要几点因素.通过学生学习成绩预测模型,及时对学生学习情况进行分析.实验结果表明,本实验的学生学习动态模型能较为准确地对学生成绩进行预测,并能够及时得出影响学生学习的主要几点因素.有利于老师及时了解学生情况并及时对教学配置和教学指导进行调整,提高学生的学习效率.
关键词：MOOC平台；最邻近算法；IBK算法；等频装箱法；GBDT模型；SVM模型；随机森林模型
中图分类号： G434 文献标识码：A 文章编号：1009-3044（2018）10-0149-04
Learning Effect Prediction Based on Online Learning Behior
WU Wei-wang
（Tianjin University Of Finance & Economics Polytechnic College, Tianjin 300222, China）
Abstract： With the continuous development of the Internet, more and more people choose to learn knowledge through the MOOC platform. However, on the Internet, it is difficult for teachers to know the learning situation of the learners, which is not conducive to the adjustment of the allocation of teaching resources and the teaching process. This study proposes to establish a dynamic model of students" learning, to analyze the students" learning situation and to feed back the results in time. In this study, we first use the IBK algorithm in the nearest neighbor algorithm. The abnormal data of students" behior data are excluded, and the students" learning performance and learning behior data are processed by the equal frequency packing method. The GBDT model and SVM model are used to establish the students" learning performance prediction model. At last, the random forest model is used to train the data, and the influence factors model of students" learning achievement is set up, and the model is analyzed. Through the analysis of the influence factor model of students" learning achievement, we find out the main factors that affect the students" academic achievement. The students" learning performance is analyzed in time by the model of students" learning achievement prediction. The experimental results show that the student learning dynamic model in this experiment can accurately predict student achievement, and timely draw the main factors that affect student learning. It is helpful for teachers to understand the students" situation in a timely manner and adjust the teaching arrangement and instruction in time to improve the students" learning efficiency.
Key words： MOOC platform, Nearest neighbor algorithm, IBK algorithm, Equal frequency packing method, GBDT model , SVM model, Logistic regression, Random Forest
结论：适合不知如何写学习效果预测方面的相关专业大学硕士和本科毕业论文以及关于学习效果预测论文开题报告范文和相关职称论文写作参考文献资料下载。
在线学习行为分析综述
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