Personalized education, tailored to individual student needs, leverages educational
technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness …

In the last years, the weakly supervised paradigm of multiple instance learning (MIL) has
become very popular in many different areas. A paradigmatic example is computational …

Knowledge tracing, the goal of which is predicting students' future performance given their
past question response sequences to trace their knowledge states, is pivotal for computer …

Knowledge tracing (KT), the task of tracking the knowledge state of a student over time, has
been assessed actively by artificial intelligence researchers. Recent reports have described …

Knowledge tracing aims to trace students' evolving knowledge states by predicting their
future performance on concept-related exercises. Recently, some graph-based models have …

Automated question quality rating (AQQR) aims to evaluate question quality through
computational means, thereby addressing emerging challenges in online learnersourced …

Learning structures between groups of variables from data with missing values is an
important task in the real world, yet difficult to solve. One typical scenario is discovering the …

We consider the problem of assessing the changing performance levels of individual
students as they go through online courses. This student performance (SP) modeling …

High-quality distractors are crucial to both the assessment and pedagogical value of multiple-
choice questions (MCQs), where manually crafting ones that anticipate knowledge …

Interdisciplinary research in the learning sciences incorporates diverse constructs,
measures, and processes with the goal of advancing theories of learning and to inform the …