José P. González-Brenes

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Traditionally, the assessment and learning science communities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two(More)
Classification evaluation metrics are often used to evaluate adaptive tutoring systems— programs that teach and adapt to humans. Unfortunately, it is not clear how intuitive these metrics are for practitioners with little machine learning background. Moreover, our experiments suggest that existing convention for evaluating tutoring systems may lead to(More)
This work describes a unified approach to two problems previously addressed separately in Intelligent Tutoring Systems: (i) Cognitive Modeling, which factorizes problem solving steps into the latent set of skills required to perform them [7]; and (ii) Student Modeling, which infers students’ learning by observing student performance [9]. The practical(More)
We present the Topical Hidden Markov Model method, which infers jointly a cognitive and student model from longitudinal observations of student performance. Its cognitive diagnostic component specifies which items use which skills. Its knowledge tracing component specifies how to infer students’ knowledge of these skills from their observed performance.(More)
Latent variable models, such as the popular Knowledge Tracing method, are often used to enable adaptive tutoring systems to personalize education. However, finding optimal model parameters is usually a difficult non-convex optimization problem when considering latent variable models. Prior work has reported that latent variable models obtained from(More)
Knowledge Tracing is the de-facto standard for inferring student knowledge from performance data. Unfortunately, it does not allow modeling the feature-rich data that is now possible to collect in modern digital learning environments. Because of this, many ad hoc Knowledge Tracing variants have been proposed to model a specific feature of interest. For(More)
Online education provides data from students solving problems at different levels of proficiency over time. Unfortunately, methods that use these data for inferring student knowledge rely on costly domain expertise. We propose three novel data-driven methods that bridge sequence modeling with topic models to infer students’ time varying knowledge. These(More)