A Comparison of Machine Learning Techniques for Validating Students’ Proficiency in Mathematics
Date of Degree
Data Analysis & Visualization
Howard T. Everson
Data Science | Educational Assessment, Evaluation, and Research | Educational Methods
machine learning, math proficiency, multinominal logistic regression, SPSS Modeler
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.
Avdeev, Alexander, "A Comparison of Machine Learning Techniques for Validating Students’ Proficiency in Mathematics" (2022). CUNY Academic Works.
This work is embargoed and will be available for download on Friday, June 09, 2023
Graduate Center users:
To read this work, log in to your GC ILL account and place a thesis request.
See the GC’s lending policies to learn more.
Data Science Commons, Educational Assessment, Evaluation, and Research Commons, Educational Methods Commons