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

## TL;DR

This study demonstrates that hidden Markov models can effectively analyze and predict student performance over time in educational games, providing insights beyond traditional cross-sectional assessments.

## Contribution

The paper introduces the use of hidden Markov models to analyze time series data from educational games, enabling dynamic performance prediction.

## Key findings

- State trajectories accurately predict student performance.
- Hidden Markov models outperform traditional methods.
- Effective for different student groups.

## 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 paper shows that students' knowledge throughout the intervention can be estimated by time series analysis using a hidden Markov model. 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 a hidden Markov model be used to analyze the educational games? Can a hidden Markov model 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 test 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 class 1 and class 2.