Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning meets Assessment: On the relation between Item Response Theory
  and Bayesian Knowledge Tracing   Few models have been more ubiquitous in their respective fields than Bayesian
knowledge tracing and item response theory. Both of these models were developed
to analyze data on learners. However, the study designs that these models are
designed for differ; Bayesian knowledge tracing is designed to analyze
longitudinal data while item response theory is built for cross-sectional data.
This paper illustrates a fundamental connection between these two models.
Specifically, the stationary distribution of the latent variable and the
observed response variable in Bayesian knowledge Tracing are related to an item
response theory model. This connection between these two models highlights a
key missing component: the role of education in these models. A research agenda
is outlined which answers how to move forward with modeling learner data.
%Furthermore, recent advances in network psychometrics demonstrate how this
relationship can be exploited and generalized to a network model.

20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
1
The categories of this paper are: cs
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th