English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Few models have been more ubiquitous in their respective fields than Bayesianknowledge tracing and item response theory. Both of these models were developedto analyze data on learners. However, the study designs that these models aredesigned for differ; Bayesian knowledge tracing is designed to analyzelongitudinal 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 theobserved response variable in Bayesian knowledge Tracing are related to an itemresponse theory model. This connection between these two models highlights akey missing component: the role of education in these models. A research agendais outlined which answers how to move forward with modeling learner data.%Furthermore, recent advances in network psychometrics demonstrate how thisrelationship can be exploited and generalized to a network model.

Learning meets Assessment: On the relation between Item Response Theory and Bayesian Knowledge Tracing