I am a phd student at the Arizona State University, and I am trying to integrate a dynamic Bayesian network (+HMM) in an educational video game that I made using Unity3D. However, I am not well versed in Bayesian and has just started reading about it a few months ago and is thus facing lot of issues while trying to implement it in my game.
The problem that I am trying to model is explained below:
When the level starts they come with a prior knowledge about the topic. For example, if a player is playing a game about balancing chemical equations, then they come with an existing prior knowledge about whether they know how to balance it or not (say P(L) is zero, that they know nothing about it). When they start playing the game, they balance an equation by picking up the molecules that are required to balance the equation, they may pick some distractors (Neon molecules or extra reactants, products during the process). Once they have picked up all the required reactant and product molecules in the desired quantity a quiz appears which asks them to balance the chemical equation. Now they can answer this as right or wrong( i.e. P(Q) is either 0 or 1, depending on whether they answered it correctly or incorrectly). Also, there is a hidden node called probability of knowledge(P(K)) which also has two states (0 and 1), and denotes the latent variable whether they have the required knowledge to balance a chemical equation or not. It may happen that they have the knowledge but answer the question wrongly which is called the probability of slip (P(S)), or they may guess correctly despite having P(K)=0. Now these are the states and nodes for just level 1, there are 5 more levels in the game (total of 6). The learn rate is the probability that a student will transition between the unlearned P(K)=0 and the learned state P(K)=1 after each learning opportunity (or question). The aim of my study is to learn the performance parameters P(S) and P(G), and the learn rate of a student.
Later to add complexity I will include the nodes for distractor as well (which are present in varying quantity on each level), but for now I am just modeling the Bayesian network which is based on this article - http://users.wpi.edu/~zpardos/papers/UMAP_final.pdf
I was looking at the offerings page on the website and I was wondering if you could help me in solving the problem in any manner.
Following is a screenshot of the game showing level 1 when player attempts to pick up a distractor.
Following is a screenshot of the game when quiz appears on level 1:
This is how the network looks likes:
