Machine Learning and Student Performance in Teams

被引：3

作者：

Ahuja, Rohan ^{[1
]}

Khan, Daniyal ^{[1
]}

Tahir, Sara ^{[1
]}

Wang, Magdalene ^{[1
]}

Symonette, Danilo ^{[1
]}

Pan, Shimei ^{[1
]}

Stacey, Simon ^{[1
]}

Engel, Don ^{[1
]}

机构：

[1] Univ Maryland Baltimore Cty, Baltimore, MD 21228 USA

来源：

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2020), PT II | 2020年 / 12164卷

基金：

美国国家科学基金会;

关键词：

Machine learning; Teamwork; Performance prediction; Text mining; TEAMWORK;

D O I：

10.1007/978-3-030-52240-7_55

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：301 / 305

页数：5

共 13 条

[1] Learning to monitor and regulate collective thinking processes [J].