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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Paper Title:	A Review on Random Forest Measurements To Assess And Predict Student Learning of Software Engineering Teamwork
Authors Name:	Karthik Suresh , Jibin Biju , Karthika Santhosh
Unique Id:	IJSDR2303261
Published In:	Volume 8 Issue 3, March-2023
Abstract:	Abstract: The overall thing of our Software Engineering Teamwork Assessment and Prediction (SETAP) design is to develop effective machine- literacy- grounded styles for assessment and early prediction of pupil learning effectiveness in software engineering teamwork. Through this paper we briefly present a review on SETAP using Random Forest algorithm done at San Francisco State University (SFSU), Fulda University, Florida Atlantic University (FAU). These data are grouped into 11 time intervals, each measuring important phase of design development during the class. Results show that they're suitable to descry pupil teams who are bound to fail or need attention in early class time with good delicacy also the variable significance analysis shows that the features with high prophetic power. These measures can be used to guide preceptors and software engineering directors to insure early intervention for teams bound to fail.
Keywords:	Index Terms: software engineering teamwork, prediction, machine learning, education .
Cite Article:	"A Review on Random Forest Measurements To Assess And Predict Student Learning of Software Engineering Teamwork", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 3, page no.1488 - 1494, March-2023, Available :http://www.ijsdr.org/papers/IJSDR2303261.pdf
Downloads:	000337354
Publication Details:	Published Paper ID: IJSDR2303261 Registration ID:204989 Published In: Volume 8 Issue 3, March-2023 DOI (Digital Object Identifier): Page No: 1488 - 1494 Publisher: IJSDR \| www.ijsdr.org ISSN Number: 2455-2631

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