Machine Learning and Student Performance in Teams

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dc.contributor.authorAhuja, Rohan
dc.contributor.authorKhan, Daniyal
dc.contributor.authorTahir, Sara
dc.contributor.authorWang, Magdalene
dc.contributor.authorSymonette, Danilo
dc.contributor.authorPan, Shimei
dc.contributor.authorStacey, Simon
dc.contributor.authorEngel, Don
dc.date.accessioned2021-04-29T18:26:28Z
dc.date.available2021-04-29T18:26:28Z
dc.date.issued2020-06-30
dc.descriptionInternational Conference on Artificial Intelligence in Education, AIED 2020: Artificial Intelligence in Education pp 301-305en_US
dc.description.abstractThis 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.en_US
dc.description.urihttps://link.springer.com/chapter/10.1007/978-3-030-52240-7_55en_US
dc.format.extent5 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m25t9a-anfk
dc.identifier.citationAhuja 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_55en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-52240-7_55
dc.identifier.urihttp://hdl.handle.net/11603/21405
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Office for the Vice President of Research
dc.rightsThis 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.
dc.subjectMachine learningen_US
dc.subjectTeamworken_US
dc.subjectPerformance predictionen_US
dc.subjectText miningen_US
dc.titleMachine Learning and Student Performance in Teamsen_US
dc.typeTexten_US

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