Enhancing the efficiency and reliability of group differentiation through partial credit
Enhancing the efficiency and reliability of group differentiation through partial credit
复制标题
通过部分信贷提高集团差异化的效率和可靠性
DOI：
10.1145/2883851.2883910
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
N. Heffernan
中科院分区：
文献类型：
--
作者：
Yan Wang;Korinn S. Ostrow;J. Beck;N. Heffernan
学习分析社区的重点是弥合受控教育研究和数据挖掘之间的差距。在线学习平台可用于进行随机对照试验，以帮助开发增加学习收益的干预措施;来自此类研究的数据集可以作为好奇的数据挖掘者的宝库。目前的工作采用了数据挖掘方法随机对照试验数据从ASSISTments，一个流行的在线学习平台，以评估的好处，将额外的学生成绩数据时，试图区分两个用户组。通过一个restaurant技术，我们表明，部分信贷，定义为二进制的正确性，提示的使用和尝试计数的算法组合，可以受益于评估和组分化。部分信用减少了可靠区分已知不同的组所需的样本量58%，并减少了可靠区分不太明显的组所需的样本量9%。
The focus of the learning analytics community bridges the gap between controlled educational research and data mining. Online learning platforms can be used to conduct randomized controlled trials to assist in the development of interventions that increase learning gains; datasets from such research can act as a treasure trove for inquisitive data miners. The present work employs a data mining approach on randomized controlled trial data from ASSISTments, a popular online learning platform, to assess the benefits of incorporating additional student performance data when attempting to differentiate between two user groups. Through a resampling technique, we show that partial credit, defined as an algorithmic combination of binary correctness, hint usage, and attempt count, can benefit assessment and group differentiation. Partial credit reduces sample sizes required to reliably differentiate between groups that are known to differ by 58%, and reduces sample sizes required to reliably differentiate between less distinct groups by 9%.