Examining Student Effort on Help through Response Time Decomposition

Examining Student Effort on Help through Response Time Decomposition
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通过响应时间分解检查学生对帮助的努力

DOI:
10.1145/3448139.3448167
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发表时间:
2021
期刊:
Proceedings of the 11th International Conference on Learning Analytics and Knowledge
影响因子:
--
通讯作者:
Heffernan, Neil T.
Heffernan, Neil T.
中科院分区:
--
文献类型:
--
作者:
Gurung, Ashish;Botelho, Anthony F.;Heffernan, Neil T.

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许多教师已经开始依赖基于计算机的学习平台在帮助学生评估、补充教学以及在学生完成指定内容时向他们提供即时反馈和帮助方面提供的负担。同样,研究人员通常利用描述学生与平台互动的点击流日志的大型数据集来研究学习。对于使用这些信息来监控学生进度的教师以及研究人员来说,这些数据提供了对学习过程的有限洞察;尤其是在观察和理解学生在工作中所付出的努力方面。从教师的角度来看,重要的是知道哪些学生正在学习和使用计算机提供的帮助,哪些学生正在利用系统完成工作,而不是有效地学习材料。在本文中,我们基于响应时间分解(RTD)进行了一系列分析,以探索计算机学习平台中按需提示背景下的学生求助行为,重点考察哪些学生在参与系统时表现出了学习努力。然后,我们的发现被用来检验我们对学生努力的衡量标准如何与后来的学生表现衡量标准相关联。
Many teachers have come to rely on the affordances that computer-based learning platforms offer in regard to aiding in student assessment, supplementing instruction, and providing immediate feedback and help to students as they work through assigned content. Similarly, researchers commonly utilize the large datasets of clickstream logs describing students’ interactions with the platform to study learning. For the teachers that use this information to monitor student progress, as well as for researchers, this data provides limited insights into the learning process; this is particularly the case as it pertains to observing and understanding the effort that students are applying to their work. From the perspective of teachers, it is important for them to know which students are attending to and using computer-provided aid and which are taking advantage of the system to complete work without effectively learning the material. In this paper, we conduct a series of analyses based on response time decomposition (RTD) to explore student help-seeking behavior in the context of on-demand hints within a computer-based learning platform with particular focus on examining which students appear to be exhibiting effort to learn while engaging with the system. Our findings are then leveraged to examine how our measure of student effort correlates with later student performance measures.
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