Ahuja, RohanKhan, DaniyalTahir, SaraWang, MagdaleneSymonette, DaniloPan, ShimeiStacey, SimonEngel, Don2021-04-292021-04-292020-06-30Ahuja R. et al. (2020) Machine Learning and Student Performance in Teams. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12164. Springer, Cham. https://doi.org/10.1007/978-3-030-52240-7_55https://doi.org/10.1007/978-3-030-52240-7_55http://hdl.handle.net/11603/21405International Conference on Artificial Intelligence in Education, AIED 2020: Artificial Intelligence in Education pp 301-305This project applies a variety of machine learning algorithms to the interactions of first year college students using the GroupMe messaging platform to collaborate online on a team project. The project assesses the efficacy of these techniques in predicting existing measures of team member performance, generated by self- and peer assessment through the Comprehensive Assessment of Team Member Effectiveness (CATME) tool. We employed a wide range of machine learning classifiers (SVM, KNN, Random Forests, Logistic Regression, Bernoulli Naive Bayes) and a range of features (generated by a socio-linguistic text analysis program, Doc2Vec, and TF-IDF) to predict individual team member performance. Our results suggest machine learning models hold out the possibility of providing accurate, real-time information about team and team member behaviors that instructors can use to support students engaged in team-based work, though challenges remain.5 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Machine learningTeamworkPerformance predictionText miningText