Distributional Efficacy of Collaborative Learning

What is it about?

This study addresses self-selection and heterogeneity issues inherent in measuring the efficacy of voluntary training programs. We exploit data collected from Indiana University's introductory microeconomics course. In conjunction with their class, undergraduates were given the choice to participate in a voluntary training program called Collaborative Learning (CL), which is designed to encourage a self-discovery learning style. To address self-selection and heterogeneity in the effectiveness of CL, program evaluation methods were used to measure student performance. We find, amongst other things, that CL produces heterogeneous results e.g., the bottom 40 percentile of CL participants improved their performance the most, and that students at the higher end of the grade distribution achieve greater improvement in topic understanding. The latter is greater than can be associated with superior innate ability alone. Finally, parametric and non-parametric sensitivity analysis confirmed that the sign of the calculated treatment effects is robust to potential violations of the underlying assumptions.

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Why is it important?

This study found that Collaborative Learning (CL) produces interesting results related to student rankings in the course. For example, the bottom 40 percentile of CL participants improved their course performance the most. In addition, students at the higher end of the grade distribution achieved greater improvement in overall mastery of the course content and development of economic reasoning beyond the specific learning objectives of the course. The latter effect is larger than an effect that can be associated with superior innate ability alone. This speaks well of the role this structure can play in fostering student leaning. CL tends to help all students who participate and is likely to improve economic reasoning beyond what can be reflected in the course grade alone.