arXiv subjects

Adam C Sales

Publications and source records attributed to Adam C Sales.

5 recordsLinked to original sources

arXiv2025

E-TRIALS: Empowering Data-Driven Decisions to Enhance Computer-Based Learning Platforms

Computer-based learning platforms (CBLPs) have become a common medium in schools, transforming how students learn and interact with educational content. However, researchers still lack adequate tools to address the diverse set of challenges that students face in these environments. In this paper, we introduce \textbf{Ed-Tech Research Infrastructure to Advance Learning Sciences (E-TRIALS)}, a free tool developed by ASSISTments to help researchers conduct randomized controlled trials in the realm of learning sciences. We describe its features, the types of experiments it supports, and how it can address critical research questions. We showcase E-TRIALS' capabilities through two real-world interventions. Finally, we evaluate the efficacy of interventions using three average treatment effect (ATE) estimators. Student's t-test, regression, and Leave-One-Out Potential outcomes (LOOP). The results demonstrate that the unbiased LOOP estimator can achieve greater precision by adjusting for baseline covariates compared to the Student's t test. Our work demonstrates the potential of E-TRIALS to advance research and contribute to the development of more effective, inclusive, and adaptive CBLP. The code used for this work is available at https://osf.io/xp6ch/.

stat.AP↗

arXiv2023

Fully Latent Principal Stratification With Measurement Models

There is wide agreement on the importance of implementation data from randomized effectiveness studies in behavioral science; however, there are few methods available to incorporate these data into causal models, especially when they are multivariate or longitudinal, and interest is in low-dimensional summaries. We introduce a framework for studying how treatment effects vary between subjects who implement an intervention differently, combining principal stratification with latent variable measurement models; since principal strata are latent in both treatment arms, we call it "fully-latent principal stratification" or FLPS. We describe FLPS models including item-response-theory measurement, show that they are feasible in a simulation study, and illustrate them in an analysis of hint usage from a randomized study of computerized mathematics tutors.

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arXiv2018

Student Log-Data from a Randomized Evaluation of Educational Technology: A Causal Case Study

Randomized evaluations of educational technology produce log data as a bi-product: highly granular data student and teacher usage. These datasets could shed light on causal mechanisms, effect heterogeneity, or optimal use. However, there are methodological challenges: implementation is not randomized and is only defined for the treatment group, and log datasets have a complex structure. This paper discusses three approaches to help surmount these issues. One approach uses data from the treatment group to estimate the effect of usage on outcomes in an observational study. Another, causal mediation analysis, estimates the role of usage in driving the overall effect. Finally, principal stratification estimates overall effects for groups of students with the same "potential" usage. We analyze hint data from an evaluation of the Cognitive Tutor Algebra I curriculum using these three approaches, with possibly conflicting results: the observational study and mediation analysis suggest that hints reduce posttest scores, while principal stratification finds that treatment effects may be correlated with higher rates of hint requests. We discuss these mixed conclusions and give broader methodological recommendations.

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arXiv2018

Mastery Learning in Practice: A (Mostly) Descriptive Analysis of Log Data from the Cognitive Tutor Algebra I Effectiveness Trial

Mastery learning, the notion that students learn best if they move on from studying a topic only after having demonstrated mastery, sits at the foundation of the theory of intelligent tutoring. This paper is an exploration of how mastery learning plays out in practice, based on log data from a large randomized effectiveness trial of the Cognitive Tutor Algebra I (CTAI) curriculum. We find that students frequently progressed from CTAI sections they were working on without demonstrating mastery and worked units out of order. Moreover, these behaviors were substantially more common in the second year of the study, in which the CTAI effect was significantly larger. We explore the various ways students departed from the official CTAI curriculum, focusing on heterogeneity between years, states, schools, and students. The paper concludes with an observational study of the effect on post-test scores of teachers reassigning students out of their current sections before they mastered the requisite skills, finding that reassignment appears to lowers posttest scores--a finding that is fairly resilient to confounding from omitted covariates--but that the effect varies substantially between classrooms.

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arXiv2015

Rebar: Reinforcing a Matching Estimator with Predictions from High-Dimensional Covariates

In causal matching designs, some control subjects are often left unmatched, and some covariates are often left unmodeled. This article introduces "rebar," a method using high-dimensional modeling to incorporate these commonly discarded data without sacrificing the integrity of the matching design. After constructing a match, a researcher uses the unmatched control subjects--the remnant--to fit a machine learning model predicting control potential outcomes as a function of the full covariate matrix. The resulting predictions in the matched set are used to adjust the causal estimate to reduce confounding bias. We present theoretical results to justify the method's bias-reducing properties as well as a simulation study that demonstrates them. Additionally, we illustrate the method in an evaluation of a school-level comprehensive educational reform program in Arizona.

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