Do Most Students Need in-Person Lectures? A Study of a Large Statistics Class

Ellen S. Fireman, Zachary S. Donnini, M. Weissman, Daniel J. Eck
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Abstract

Over 1100 students over four semesters were given the option of taking an introductory undergraduate statistics class either by in-person attendance in lectures or by taking exactly the same class (same instructor, recorded lectures, homework, blind grading, website, etc.) without the in-person lectures. Roughly, equal numbers of students chose each option. The online students did slightly better on computer-graded exams. The causal effect of choosing only online lectures was estimated by adjusting for measured confounders using four standard methods. The four nearly identical point estimates remained positive but were small and not statistically significant at the 95% confidence level. Sensitivity analysis indicated that unmeasured confounding was unlikely to be large but might plausibly reduce the point estimate to zero. No statistically significant differences were found in preliminary comparisons of effects on females/males, U.S./non-U.S. citizens, freshmen/non-freshman, and lower-scoring/higher-scoring math ACT groups.
大多数学生需要亲自授课吗?一个大型统计班的研究
在四个学期中,1100多名学生可以选择参加本科统计学入门课程,或者亲自出席讲座,或者在没有亲自授课的情况下参加完全相同的课程(相同的讲师,录音讲座,作业,盲目评分,网站等)。大致上,选择这两个选项的学生人数相等。在线学生在计算机评分考试中表现稍好。仅选择在线课程的因果效应通过使用四种标准方法调整测量的混杂因素来估计。四个几乎相同的点估计值仍然是正的,但在95%的置信水平上很小,没有统计学意义。敏感性分析表明,未测量的混杂因素不太可能很大,但可能合理地将点估计值降低到零。在对女性/男性、美国/非美国人的影响的初步比较中,没有发现统计学上的显著差异。公民,新生/非新生,以及得分较低/较高的ACT数学组。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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