Recently, model-based reinforcement learning has been considered a crucial approach to applying reinforcement learning in the physical world, primarily due to its efficient utilization of samples.

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Corrected: This story has been updated to correct the number of districts participating in Washington state’s Mastery-Based Learning Collaborative. Every state now allows schools to embrace competency ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

Education experts are encouraging schools to consider problem-based learning (PBL) in a move to improve engagement and creativity among high school students. New research demonstrates how hands-on, ...

School districts in every state now have the green light to establish competency-based education programs and models in their classrooms—but they have a lot of work to do on the operational side to ...

Hydrology experts at the U.S. Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) used artificial intelligence and a physics-based understanding of streamflow to create a model that ...

This article examines the work of data scientist Sai Prashanth Pathi in AI for credit risk, focusing on explainable machine learning in regulated finance, governance alignment, fairness, compliance, ...

When classrooms went online in 2020 during the pandemic, gamified learning opportunities grew as a quick and easy way to evaluate the general learning that was happening from student to student.