A very active group of STEM education researchers have developed and deployed numerous teaching innovations in the last few decades, significantly changing STEM instruction (Simkins and Maier 2008). Some have shown learning gains on the order of 2 standard deviations (Hake 1998; Deslauriers, Schelew, and Wieman 2011). These innovations, often dubbed "research-based" or "evidence-based" teaching methods, employ findings from cognitive science. Thus, their underpinnings are more fundamental than "flipping" or "active learning." Might employing these findings in an economics classroom lead to similar gains? This paper addresses this question in a redesigned macro principles course, with hundreds of students, taught by the second author.I. Limited Learning by Economics and Physics StudentsWalstad and Allgood (1999) show that students are not retaining much knowledge from their economics courses. A similar finding is suggested by the norming process of the "Test of Understanding of College Economics" (TUCE) (Walstad, Watts, and Rebeck 2007). Thousands of students from dozens of institutions show modest improvement on this 30-question assessment over the course of the semester. On the micro version, students went from answering 9.39 questions correctly at the start of the semester to 12.77 at the end, while macro students went from 9.80 to 14.19.
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机译：在过去的几十年中，一群非常活跃的STEM教育研究人员开发并部署了许多教学创新，从而极大地改变了STEM的教学方式（Simkins and Maier 2008）。有些人的学习成绩大约是2个标准差（Hake 1998； Deslauriers，Schelew和Wieman 2011）。这些创新通常被称为“基于研究”或“基于证据”的教学方法，它们利用了来自认知科学的发现。因此，它们的基础比“翻转”或“主动学习”更为根本。在经济学课堂上运用这些发现可能会带来类似的收获吗？本文在重新设计的宏观原理课程中解决了这个问题，该课程有数百名学生，由第二作者讲授。经济学和物理学专业学生的有限学习Walstad和Allgood（1999）表明，学生在经济学课程中的知识并不多。 “对大学经济学的理解测验”（TUCE）的定标过程也表明了类似的发现（Walstad，Watts和Rebeck，2007年）。在整个学期中，来自数十个机构的成千上万的学生在这30个问题的评估中显示出适度的提高。在微型课程中，学生从上学期开始时正确回答9.39个问题，到结束时为12.77个问题，而宏观类学生则从9.80人达到14.19个问题。
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