Work

Tracing Player Knowledge in a Parallel Programming Educational Game

Year: 2018

Type: conference-paper

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.

Source: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

DOI: 10.1609/aiide.v14i1.13038

Authors: Pavan Kantharaju, Katelyn Alderfer, Jichen Zhu, Bruce Char, Brian Smith, Santiago Ontanon

Institution: Drexel University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: bronze

Funder: National Science Foundation