Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and…

We explored a series of feature selection methods for model-based Reinforcement Learning (RL). More specifically, we explored four common correlation metrics and based on them, we proposed the fifth one named Weighed Information Gain (WIG). While much existing correlation-based feature selection methods mostly explored high correlation by default,…

In this work we tackled the task of Automatic Short Answer Grading (ASAG). While conventional ASAG research makes prediction mainly based on student answers referred as Answer-based, we leveraged the information about questions and student models into consideration. More specifically, we explore the Answer-based, Question, and Student models…

Bayesian Knowledge Tracing (BKT) is a commonly used approach for student modeling, and Long Short Term Memory (LSTM) is a versatile model that can be applied to a wide range of tasks, such as language translation. In this work, we directly compared three models: BKT, its variant Intervention-BKT (IBKT), and LSTM, on two types of student modeling…

Early prediction of student difficulty during long-duration learning activities allows a tutoring system to intervene by providing needed support, such as a hint, or by alerting an instructor. To be effective, these predictions must come early and be highly accurate, but such predictions are difficult for open-ended programming problems. In this…