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There is a growing need to empirically evaluate the quality of online instructional interventions at scale. In response, some online learning platforms have begun to implement rapid A/B testing of instructional interventions. In these scenarios, students participate in series of randomized ex- periments that evaluate problem-level interventions in quick succession, which makes it difficult to discern the effect of any particular intervention on their learning. Therefore, dis- tal measures of learning such as posttests may not provide a clear understanding of which interventions are effective, which can lead to slow adoption of new instructional meth- ods. To help discern the effectiveness of instructional in- terventions, this work uses data from 26,060 clickstream se- quences of students across 31 different online educational experiments exploring 51 different research questions and the students’ posttest scores to create and analyze different proximal surrogate measures of learning that can be used at the problem level. Through feature engineering and deep learning approaches, next problem correctness was deter- mined to be the best surrogate measure. As more data from online educational experiments are collected, model based surrogate measures can be improved, but for now, next prob- lem correctness is an empirically effective proximal surrogate measure of learning for analyzing rapid problem-level exper- iments. 

Many online learning platforms and MOOCs incorporate some amount of video-based content into their platform, but there are few randomized controlled experiments that evaluate the effective- ness of the different methods of video integration. Given the large amount of publicly available educational videos, an investigation into this content’s impact on students could help lead to more ef- fective and accessible video integration within learning platforms. In this work, a new feature was added into an existing online learn- ing platform that allowed students to request skill-related videos while completing their online middle-school mathematics assign- ments. A total of 18,535 students participated in two large-scale randomized controlled experiments related to providing students with publicly available educational videos. The first experiment investigated the effect of providing students with the opportunity to request these videos, and the second experiment investigated the effect of using a multi-armed bandit algorithm to recommend relevant videos. Additionally, this work investigated which features of the videos were significantly predictive of students’ performance and which features could be used to personalize students’ learning. Ultimately, students were mostly disinterested in the skill-related videos, preferring instead to use the platforms existing problem- specific support, and there was no statistically significant findings in either experiment. Additionally, while no video features were significantly predictive of students’ performance, two video fea- tures had significant qualitative interactions with students’ prior knowledge, which showed that different content creators were more effective for different groups of students. These findings can be used to inform the design of future video-based features within online learning platforms and the creation of different educational videos specifically targeting higher or lower knowledge students. The data and code used in this work is hosted by the Open Science Foundation. 

This paper will explain how analyzing experiments as a group can improve estimation and inference of causal effects– even when the experiments are testing unrelated treatments. The method, composed of ideas from meta-analysis, shrinkage estimators, and Bayesian hierarchical modeling, is particularly relevant in studies of educational technology. Analyzing experiments as a group–”partially pooling” their respective datasets–increases overall accuracy and avoids issues of multiple comparisons, while incurring small bias. The paper will explain how the method works, demonstrate it on a set of randomized experiments run within the ASSISTments platform, and illustrate its properties in a simulation study. 

Randomized A/B tests in educational software are not run in a vacuum: often, reams of historical data are available alongside the data from a randomized trial. This paper proposes a method to use this historical data–often high dimensional and longitudinal–to improve causal estimates from A/B tests. The method proceeds in two steps: first, fit a machine learning model to the historical data predicting students’ outcomes as a function of their covariates. Then, use that model to predict the outcomes of the randomized students in the A/B test. Finally, use design-based methods to estimate the treatment effect in the A/B test, using prediction errors in place of outcomes. This method retains all of the advantages of design-based inference, while, under certain conditions, yielding more precise estimators. This paper will give a theoretical condition under which the method improves statistical precision, and demonstrates it using a deep learning algorithm to help estimate effects in a set of experiments run inside ASSISTments.