Learning Analytics focuses on the collection and analysis of learners' data to improve their
learning experience by providing informed guidance and to optimise learning materials. To …

Over the last couple of decades, there have been a large variety of approaches towards
modeling student knowledge within intelligent tutoring systems. With the booming …

Computational sprinting is a class of mechanisms that boost performance but dissipate
additional power. We describe a sprinting architecture in which many, independent chip …

We introduce a new sub-linear space sketch---the Weight-Median Sketch---for learning
compressed linear classifiers over data streams while supporting the efficient recovery of …

Exploitation of parallel architectures has become critical to scalable machine learning (ML).
Since a wide range of ML algorithms employ linear algebraic operators, GPUs with BLAS …

Sequence-wise recommendation, where recommend exercises to each student step by step,
is one of the most exciting tasks in the field of intelligent tutoring systems (ITS). It is important …

This competition concerns educational diagnostic questions, which are pedagogically
effective, multiple-choice questions (MCQs) whose distractors embody misconceptions. With …

Personalized learning environments requiring the elicitation of a student's knowledge state
have inspired researchers to propose distinct models to understand that knowledge state …

Online learning methods, like the seminal Passive-Aggressive (PA) classifier, are still highly
effective for high-dimensional streaming data, out-of-core processing, and other throughput …

Multidimensional item response theory (MIRT) is a statistical test theory that precisely
estimates multiple latent skills of learners from the responses in a test. Both compensatory …