XGBoost and Deep Neural Network Comparison: The Case of Teams' Performance

In the educational setting, working in teams is considered an essential collaborative activity where various biases exist that influence the prediction of teams performance. To tackle this issue, machine learning algorithms can be properly explored and utilized. In this context, the main objective of the current paper is to explore the ability of the eXtreme Gradient Boosting (XGBoost) algorithm and a Deep Neural Network (DNN) with 4 hidden layers to make predictions about the teams' performance. The major finding of the current paper is that shallow machine learning performed better learning and prediction results than the DNN. Specifically, the XGBoost learning accuracy was found to be 100% during teams learning and production phase, while its prediction accuracy was found to be 95.60% and 93.08%, respectively for the same phases. Similarly, the learning accuracy of the DNN was found to be 89.26% and 81.23%, while its prediction accuracy was found to be 80.50% and 77.36%, during the two phases.

Automated summary

Team collaboration is crucial in educational settings, where machine learning algorithms play a significant role in addressing biases in predicting team performance. The study reveals that the XGBoost algorithm outperforms the Deep Neural Network in learning and predicting team performance, achieving higher accuracy rates.