# Tracing Player Knowledge in a Parallel Programming Educational Game

## TL;DR

This paper presents a novel method combining machine learning and domain rules to trace player knowledge in an educational game, effectively predicting skill mastery with low error.

## Contribution

It introduces an integrated approach for knowledge tracing in educational games, specifically applying it to parallel programming education.

## Key findings

- Accurately predicts player skills with low mean-squared error
- Successfully applied to real user data in a parallel programming game
- Enhances knowledge tracing by combining machine learning with domain rules

## Abstract

This paper focuses on "tracing player knowledge" in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the paper is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called "Parallel" to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error.