用数据驱动重设计数学辅导系统，即使不选优单元也有效。
Evaluating a Data-Driven Redesign Process for Intelligent Tutoring Systems
- 按主题选4个单元，不挑优劣直接重设计。
- 123名学生实测：重设计版学习时间更高效，练更多技能，掌握更多知识。
- 证明该方法普适性强，适合大规模教育系统优化。
以往研究定义了数据驱动重设计教育技术的通用流程，并在特定案例中验证其有效性。本文将该方法应用于中学数学智能辅导系统的四个单元，这些单元按主题选取，而非基于改进潜力。通过包含123名学生的课堂实验，测试重设计系统是否优于原版。结果显示，两组学习成效无显著差异，但使用重设计辅导系统的学生成绩更优：单位时间内更具生产力、练习了更多技能、整体知识掌握度更高。结果表明，该方法在未预先筛选高潜力单元的情况下仍具有效性，凸显其广泛适用性和可推广性。
原文摘要 · Abstract (English)
Past research has defined a general process for the data-driven redesign of educational technologies and has shown that in carefully-selected instances, this process can help make systems more effective. In the current work, we test the generality of the approach by applying it to four units of a middle-school mathematics intelligent tutoring system that were selected not based on suitability for redesign, as in previous work, but on topic. We tested whether the redesigned system was more effective than the original in a classroom study with 123 students. Although the learning gains did not differ between the conditions, students who used the Redesigned Tutor had more productive time-on-task, a larger number of skills practiced, and greater total knowledge mastery. The findings highlight the promise of data-driven redesign even when applied to instructional units *not* selected as likely to yield improvement, as evidence of the generality and wide applicability of the method.
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