*Bounty: 100*

*Bounty: 100*

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).

I am making an educational video game and have no prior practical experience with the Bayesian networks. My game has 5 levels, each level has a quiz in the end. I will ask a question in the beginning of each level to gauge their prior knowledge, so that I do not have to assign a random or same value for the prior knowledge parameter for all the students. I am planning to assign a value of 0.5 to each of the guess, slip and learn rates in the beginning. As the student answers the first question I need to re-adjust the values of the guess, slip and learn rates. I will then use these to adjust the game play to show more or better hints, and basically adjust the game to the level of the student. However, I am stuck right now and despite reading a lot I am not able to figure out how to go about this.

PS: I have made the game in unity and planning to use the infer.net framework for running Bayesian inference.

I just realized that guess rate can be expressed as `P(~K|Q)`

and slip rate as `P(K|~Q)`

, but what is learn rate then, how to quantify it?

Below is a screenshot of the game menu and quiz: