We conduct a large scale RCT to investigate peer e↵ects in computer assisted learning (CAL). Identification of peer e↵ects relies on three levels of randomization. It is already known that CAL improves math test scores in Chinese rural schools. We find that paired treatment improves the beneficial e↵ects of treatment for poor performers when they are paired with high performers. We test whether CAL treatment reduces the dispersion in math scores relative to controls, and we find statistically significant evidence that it does. We also demonstrate that the beneficial e↵ects of CAL could potentially be strengthened, both in terms of average e↵ect and in terms of reduced dispersion, if weak students are systematically paired with strong students during treatment. To our knowledge, this is the first time that a school intervention has been identified in which peer e↵ects unambiguously help weak students catch up with the rest of the class without imposing any learning cost on other students.


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