Our goal with respect to a student learning process is threefold: we want him to learn from his mistakes, we want him to learn from others' mistakes, and we do not want him to repeat his own mistakes. For this purpose, a learning strategy called Double Test Learning (DTL) involving three agents, two simulated pedagogical agents (the tutor and the classmate) and one real agent (the learner), was...

Digital platforms enable the observation of learning behaviors through fine-grained log traces, offering more detailed clues for analysis. In addition to previous descriptive and predictive log analysis, this study aims to simultaneously model learner activities, event time spans, and interaction levels using the proposed Hidden Behavior Traits Model (HBTM). We evaluated model performance and e...

Concept drift due to hidden changes in context complicates learning in many real world domains including financial prediction, medical diagnosis, and communication network performance. Machine learning systems addressing this problem generally use an incremental learning, on-line paradigm. An off-line, meta-learning approach to the identification of hidden context is presented. This approach us...