Discovering 'Tough Love' Interventions Despite Dropout
نویسندگان
چکیده
This paper reports an application to educational intervention of Principal Stratification, a statistical method for estimating the effect of a treatment even when there are different rates of dropout in experimental and control conditions. We consider the potential value for using principal stratification to identify “Tough Love Interventions” – interventions that have a large effect but also increase the propensity of students to drop out. This method allowed us to generate an estimate of the treatment effect in an RCT without the selection bias induced by differential attrition by restricting analysis to just the inferred “stratum” of students who would not drop out in either condition. This paper provides a case study of how to appropriate the method of principal stratification from statistics and medical research fields to educational data mining, where it has been largely absent despite increasing relevance to online learning.
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تاریخ انتشار 2016