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10.1007/978-3-030-52240-7_55 http://scihub22266oqcxt.onion/10.1007/978-3-030-52240-7_55 C7334682!7334682!C7334682     free     free     free       Artificial+Intelligence+in+Education ä ; 12164 (ä): 301-5 Nephropedia Template TP {{tp|p=C7334682|t=ä. Machine Learning and Student Performance in Teams |pdf=|usr=}}{{C7334682}} gab.com Text Machine Learning and Student Performance in Teams JO: Artificial Intelligence in Education http://www.kidney.de/pget.php?&tw=1&pmid=C7334682 #FOAvip 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. Twit Text FOAVip Machine Learning and Student Performance in Teams ????Artificial Intelligence in Education http://www.kidney.de/pget.php?&tw=1&pmid=C7334682 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=C7334682 #FOAvip English Wikipedia <ref>{{Cite pmid|C7334682}}</ref> Machine Learning and Student Performance in Teams #MMPMIDC7334682 Ahuja R; Khan D; Tahir S; Wang M; Symonette D; Pan S; Stacey S; Engel D Artificial Intelligence in Education ä[]; 12164 (ä): 301-5 PMIDC7334682show ga 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. äMachine Learning and Student Performance in Teams (epmc).pdf DeepDyve Pubget Overpricing