# How to create a Bayesian network?

I have a question regarding a research article titles "Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing". I am trying to create a bayesian network for the model shown in this paper.

As per my understanding there is a parent node called prior knowledge, which has three child nodes namely guess rate, slip rate, and learn rate. These three nodes have a common child called question node which has two states called 'correct' and 'incorrect', depending on whether the answer to question is correct or not.

I have another viewpoint, which relates more to the figure 1 from the article, as shown below. In this view, there are three nodes. Student node, which is specific to a student and governs the prior knowledge parameter. A knowledge node (K) which has two states determining the knowledge/skill is obtained or not. A question node (Q) which again has two states, related to whether the question is answered correctly or not. Transition from K to Q is governed by the guess and slip rates, i.e. even if a student has the knowledge they can slip the question (answer it wrong) and despite being no skill they may answer it correctly (guess correctly).