Student Log-Data from a Randomized Evaluation of Educational Technology: A Causal Case Study
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The proliferation of computerized technology in education has slowly been followed by a (smaller) proliferation of evaluations of educational technology. These randomized studies are entirely classical. However, they produce an entirely new type of data as a byproduct: computer log data from subjects assigned to the treatment condition. For instance, in an effectiveness trial of the Cognitive Tutor Algebra I (CTA1) curriculum the researchers collected log data from students in the treatment group. What insights about the CTA1 effect may be extracted from this rich supplementary dataset? This paper will compare and contrast three different causal techniques in three parallel analyses of the CTA1 dataset. Specifically, we will examine the role of hints in CTA1's effect. One approach discards the control group--and hence, the randomization--and analyzes usage and outcome data in the treatment group as an observational study. Another, causal mediation analysis, contextualizes the effect of feedback in terms of the overall treatment effect. Finally, principal stratification estimates the average effect of being assigned to treatment for groups of students with the same "potential" usage.
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