Predicting Mentoring Effectiveness in a Computer Science Program: A Machine Learning Approach

Mentoring is a critical academic tool to positively influence students' outcomes. While there is a broad consensus about the benefits of mentoring, still there is a divergence of opinion regarding the attributes based on which student mentees evaluate the effectiveness of a mentoring program. T...

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Bibliographic Details
Published in2020 IEEE International Conference for Innovation in Technology (INOCON) pp. 1 - 5
Main Authors Mittal, Ruchi, Singh, Jaiteg, Mittal, Amit
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.11.2020
Subjects
Data mining
Educational Data Mining
Engineering profession
Linear Regression
Machine learning
Mentoring
Organizations
Task analysis
WEKA Machine Learning Introduction
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DOI10.1109/INOCON50539.2020.9298401

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Summary:Mentoring is a critical academic tool to positively influence students' outcomes. While there is a broad consensus about the benefits of mentoring, still there is a divergence of opinion regarding the attributes based on which student mentees evaluate the effectiveness of a mentoring program. This study has drawn a sample from undergraduate students of a computer science program and applied educational data mining to predict mentoring effectiveness. WEKA machine learning linear regression technique was applied to a primary dataset. It was observed that academic subject knowledge support was considered the most important predictor of mentoring effectiveness.
DOI:10.1109/INOCON50539.2020.9298401