The book describes up-to-date applications and relevant theoretical results. These
applications come from various places, but the most important one, numerically speaking, is …

An intelligent tutoring system for learning basic statistics, called Stat-Knowlab, is presented
and analyzed. The algorithms implemented in the system are based on the competence …

Practical applications of the theory of knowledge structures often rely on a probabilistic
version, known as the basic local independence model. The paper outlines various …

Indexes for estimating the overall reliability of a test in the framework of knowledge space
theory (KST) are proposed and analyzed. First, the possibility of applying in KST the existing …

In the process of fitting a probabilistic knowledge structure to data, standard goodness-of-fit
statistics only partially describe the correctness of the fitted model. Irrespectively of how …

The so-called basic local independence model (BLIM) constitutes the standard probabilistic
model within the theory of knowledge structures. The present paper characterizes local …

The gain–loss model (GaLoM) is a formal model for assessing knowledge and learning. In
its original formulation, the GaLoM assumes independence among the skills. Such an …

The application of the basic local independence model (BLIM) to a knowledge structure (Q,
K) that satisfies a particular kind of gradation (namely forward-or backward-gradedness) …

Missing data are a well known issue in statistical inference, because some responses may
be missing, even when data are collected carefully. The problem that arises in these cases …

Stefanutti (2019) recently developed procedures and a related theory for deriving learning
spaces from problem spaces. The approach provides a deterministic model for partially …