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Machine Learning and Student Performance in Teams

This 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 performa...

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Detalles Bibliográficos
Autores principales: Ahuja, Rohan, Khan, Daniyal, Tahir, Sara, Wang, Magdalene, Symonette, Danilo, Pan, Shimei, Stacey, Simon, Engel, Don
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334682/
http://dx.doi.org/10.1007/978-3-030-52240-7_55
Descripción
Sumario:This 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.