  This study investigates game-based learning in the context of the educational
game "Jo Wilder and the Capitol Case," focusing on predicting student
performance using various machine learning models, including K-Nearest
Neighbors (KNN), Multi-Layer Perceptron (MLP), and Random Forest. The research
aims to identify the features most predictive of student performance and
correct question answering. By leveraging gameplay data, we establish complete
benchmarks for these models and explore the importance of applying proper data
aggregation methods. By compressing all numeric data to min/max/mean/sum and
categorical data to first, last, count, and nunique, we reduced the size of the
original training data from 4.6 GB to 48 MB of preprocessed training data,
maintaining high F1 scores and accuracy.
  Our findings suggest that proper preprocessing techniques can be vital in
enhancing the performance of non-deep-learning-based models. The MLP model
outperformed the current state-of-the-art French Touch model, achieving an F-1
score of 0.83 and an accuracy of 0.74, suggesting its suitability for this
dataset. Future research should explore using larger datasets, other
preprocessing techniques, more advanced deep learning techniques, and
real-world applications to provide personalized learning recommendations to
students based on their predicted performance. This paper contributes to the
understanding of game-based learning and provides insights into optimizing
educational game experiences for improved student outcomes and skill
development.

Machine Learning;Artificial Intelligence