Predicting Math Success in an Online Tutoring System Using Language Data and Click-Stream Variables: A Longitudinal Analysis
Predicting Math Success in an Online Tutoring System Using Language Data and Click-Stream Variables: A Longitudinal Analysis
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使用语言数据和点击流变量预测在线辅导系统中数学的成功：纵向分析
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发表时间：
2019
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影响因子：
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通讯作者：
R. Baker
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文献类型：
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作者：
S. Crossley;Shamya Karumbaiah;Jaclyn L. Ocumpaugh;Matthew J. Labrum;R. Baker
先前的研究已经证明了学生的语言知识，他们的情感语言模式和他们在数学上的成功之间有很强的联系。其他研究表明，在线学习环境中的人口统计和点击流变量是数学成功的重要预测因素。本研究以两种方式建立在这项研究的基础上。首先，它将语言学和点击流变量沿着人口统计信息结合起来，以提高数学成功的预测率。其次，它研究了重复参与者数据中发现的随机方差如何解释语言、人口统计和点击流变量之外的数学成功。研究结果表明，语言，人口统计和点击流因素解释了数学成绩的14%左右的方差。这些变量与随机因素混合解释了约44%的方差。2012年ACM学科分类：应用计算→计算机辅助教学;应用计算→数学与统计;计算方法学→自然语言处理
Previous studies have demonstrated strong links between students’ linguistic knowledge, their affective language patterns and their success in math. Other studies have shown that demographic and click-stream variables in online learning environments are important predictors of math success. This study builds on this research in two ways. First, it combines linguistics and click-stream variables along with demographic information to increase prediction rates for math success. Second, it examines how random variance, as found in repeated participant data, can explain math success beyond linguistic, demographic, and click-stream variables. The findings indicate that linguistic, demographic, and click-stream factors explained about 14% of the variance in math scores. These variables mixed with random factors explained about 44% of the variance. 2012 ACM Subject Classification Applied computing → Computer-assisted instruction; Applied computing → Mathematics and statistics; Computing methodologies → Natural language processing