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Project Details

Project Title

Predicting Student Performance in an Educational Game Using a Hidden Markov Model

Abstract

Contributions: Prior studies on education have mostly followed the model of the cross-sectional study, namely, examining the pretest and the posttest scores. This article shows that students' knowledge throughout the intervention can be estimated by time-series analysis using a hidden Markov model (HMM). Background: Analyzing time series and the interaction between the students and the game data can result in valuable information that cannot be gained by only cross-sectional studies of the exams. Research Questions: Can an HMM be used to analyze the educational games? Can an HMM be used to make a prediction of the students' performance? Methodology: The study was conducted on (N = 854) students who played the Save Patch game. Students were divided into class 1 and class 2. Class 1 students are those who scored lower in the posttest than class 2 students. The analysis is done by choosing various features of the game as the observations. Findings: The state trajectories can predict the students' performance accurately for both classes 1 and 2.

Branch: CSE     Domain: Data Mining

Developed In: Java