146 citations · 183 across the 5 of their papers we have counts for
5 papers · 1 filter
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments
Timothy NeCamp, Josh Gardner, Christopher Brooks
Randomized experiments ensure robust causal inference that are critical to effective learning analytics research and practice. However, traditional randomized experiments, like A/B…
Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining
Josh Gardner, Yuming Yang, Ryan Baker +1
The use of machine learning techniques has expanded in education research, driven by the rich data from digital learning environments and institutional data warehouses. However, re…
Dropout Model Evaluation in MOOCs
Josh Gardner, Christopher Brooks
The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we presen…
Evaluating Predictive Models of Student Success: Closing the Methodological Gap
Josh Gardner, Christopher Brooks
Model evaluation -- the process of making inferences about the performance of predictive models -- is a critical component of predictive modeling research in learning analytics. We…
MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data
Josh Gardner, Christopher Brooks, Juan Miguel L. Andres +1
Big data repositories from online learning platforms such as Massive Open Online Courses (MOOCs) represent an unprecedented opportunity to advance research on education at scale an…