Technology Category
Machine Learning: Causal LearningIntelligent Robots: State EstimationCognitive Modeling & Cognitive Systems: Adaptive Behavior
E-TRIALS: Empowering Data-Driven Decisions to Enhance Computer-Based Learning Platforms
🤖 AI Summary
To address the lack of efficient, rigorous randomized controlled trial (RCT) support tools for researchers in computer-based learning platforms (CBLPs), this paper introduces E-TRIALS—an open-source educational technology research infrastructure. Methodologically, it proposes (1) the first lightweight RCT experimental framework specifically designed for CBLPs; (2) an unbiased average treatment effect (ATE) estimator based on leave-one-out potential outcomes (LOOP), which significantly improves estimation accuracy through baseline covariate adjustment—outperforming conventional t-tests in error reduction. Empirically, E-TRIALS has successfully supported two real-world instructional intervention evaluations. The implementation is fully open-sourced, enhancing reproducibility, inclusivity, and adaptive advancement in edtech research.
Technology Category
Application Category
📝 Abstract
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 extbf{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/.
Problem
Research questions and friction points this paper is trying to address.
Lack of tools for diverse student challenges in CBLPs
Need for randomized controlled trials in learning sciences
Improving intervention efficacy estimation in educational research
Innovation
Methods, ideas, or system contributions that make the work stand out.
Free tool for randomized controlled trials
Supports multiple experiment types
Uses unbiased LOOP estimator for precision
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