An important goal in the design and development of Intelligent Tutoring Systems (ITSs) is to have a system that adaptively reacts to students' behavior in the short term and effectively improves their learning performance in the long term. Inducing effective pedagogical strategies that accomplish this goal is an essential challenge. To address…

In this study, we applied decision trees (DT) to extract a compact set of pedagogical decision-making rules from an original "full" set of 3,702 Reinforcement Learning (RL)- induced rules, referred to as the DT-RL rules and Full-RL rules respectively. We then evaluated the effectiveness of the two rule sets against a baseline Random…

This study examines the use of cross-classified random effects models (CCrem) and cross-classified multiple membership random effects models (CCMMrem) to model rater bias and estimate teacher effectiveness. Effect estimates are compared using CTT versus item response theory (IRT) scaling methods and three models (i.e., conventional multilevel…

We seek to simplify the creation of intelligent tutors by using student data acquired from standard computer aided instruction (CAI) in conjunction with educational data mining methods to automatically generate adaptive hints. In our previous work, we have automatically generated hints for logic tutoring by constructing a Markov Decision Process…

The randomized response technique ensures that individual item responses, denoted as true item responses, are randomized before observing them and so-called randomized item responses are observed. A relationship is specified between randomized item response data and true item response data. True item response data are modeled with a (non)linear…