"Predicting Test Scores from Game Play Data: A Comparison of Neural Network and Regression Procedures"

AERA 2018 Annual Meeting: “The Dreams, Possibilities, and Necessity of Public Education”
April 13-17, 2018
New York

Title: "Predicting Test Scores from Game Play Data: A Comparison of Neural Network and Regression Procedures"
Authors: Gregory K.W.K. Chung, Charlie Parks, Katerina Schenke (PhD Alumna), Jeremy Roberts

Abstract

​This study examined the predictive accuracy of math test scores solely from gameplay data using either a neural network or a multiple regression procedures. Gameplay data from 56 4 and 5 year children playing 6 math games were used. The neural network procedure had an mean error of 8.3% and the multiple regression procedure a mean error of 14.7%. These results point to the promise of machine learning methods for predicting learning outcomes soley from gameplay behaviors.