Counterfactual learning of new adaptive instructional policies using logged data
In plain terms
Designing Intelligent Tutoring Systems (ITS) that effectively help students usually requires either costly real-world experiments or complex computer simulations, which might not always reflect how students actually learn. This paper introduces a method to learn improved personalized teaching strategies directly from existing student data, without needing new experiments. They treat the tutoring process as a "continuous stochastic bandit problem," which means picking the best next task for a student based on their progress, and use a statistical model called the Rasch model to understand student skill and task difficulty. They also developed a new way to measure success, called a "reward function," designed to keep students in a "flow" state by balancing challenge and success. The researchers tested their method on four large real-world datasets and consistently found that their newly learned teaching strategies were better than the original ones used to collect the data. This means improved adaptive learning policies can be found and visualized quickly using only existing information, making it easier to enhance tutoring systems.