A Comparison of Machine Learning Techniques for Validating Students’ Proficiency in Mathematics

<p>A principal goal of this project was to compare several machine learning (ML) algorithms to explore and validate math proficiency classifications based on standardized test scores. The data used in these analyses came from the 6th-grade students’ mathematics assessment records of the New York State Education Department’s Testing Program (NYSTP). Our approach was to test a number of competing machine learning (ML) algorithms for classifying students’ as proficient based on their test scores and other demographic information. Our samples were drawn from the 2016 test-taking cohort of 6th-grade students (N=156,800). Five classifiers including multinominal logistic regression (MLR), XGBoost, Tree-As, Lagrangian support vector machine (LSVM), and C5.0 Decision Tree algorithm were used to establish the best predictive model. Experimental results demonstrated that multinominal logistic regression had a better performance than other ML algorithms.</p>

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Master

Discipline thesis:degree_discipline

Data Analysis & Visualization

Grantor

The Graduate School and University Center of The City University of New York

Year dc:date.available

2022

Author dc:creator

• Avdeev, Alexander

Advisor dc:contributor.advisor

• Howard T. Everson

• Data Science
• Educational Assessment, Evaluation, and Research
• Educational Methods
• machine learning
• math proficiency
• multinominal logistic regression
• SPSS Modeler

Repository record dc:identifier

https://academicworks.cuny.edu/gc_etds/4869

OAI identifier oai:identifier

oai:academicworks.cuny.edu:gc_etds-5938