DeepAI AI Chat
Log In Sign Up

Tracing Player Knowledge in a Parallel Programming Educational Game

by   Pavan Kantharaju, et al.
Drexel University

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.


Player Skill Decomposition in Multiplayer Online Battle Arenas

Successful analysis of player skills in video games has important impact...

APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing

Knowledge tracing (KT) is a fundamental task in educational data mining ...

The PHOTON Wizard – Towards Educational Machine Learning Code Generators

Despite the tremendous efforts to democratize machine learning, especial...

Understanding Learners' Problem-Solving Strategies in Concurrent and Parallel Programming: A Game-Based Approach

Concurrent and parallel programming (CPP) is an increasingly important s...

Knowledge Query Network: How Knowledge Interacts with Skills

Knowledge Tracing (KT) is to trace the knowledge of students as they sol...

Lock-step simulation is child's play

Implementing multi-player networked games by broadcasting the player's i...