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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Avdeev, Alexander"],"institution":"The Graduate School and University Center of The City University of New York","degree_name":"Master of Science","degree_level":"Master","degree_discipline":"Data Analysis & Visualization","degree_department":null,"school":null,"contributors":[],"advisors":["Howard T. Everson"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-01T07:00:00Z","date_published":"2022-06-01T07:00:00Z","updated_at":"2026-07-24T01:59:14Z","subjects":["Data Science","Educational Assessment, Evaluation, and Research","Educational Methods","machine learning","math proficiency","multinominal logistic regression","SPSS Modeler"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/gc_etds/4869","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Howard T. Everson"]},{"key":"dc:creator","label":"Author","values":["Avdeev, Alexander"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-06-09T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Data Analysis & Visualization"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The Graduate School and University Center of The City University of New York"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data Science","Educational Assessment, Evaluation, and Research","Educational Methods","machine learning","math proficiency","multinominal logistic regression","SPSS Modeler"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/gc_etds/4869"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["A Comparison of Machine Learning Techniques for Validating Students’ Proficiency in Mathematics"]}]}],"canonical_facts":{"dc:contributor.advisor":["Howard T. Everson"],"dc:creator":["Avdeev, Alexander"],"dc:date.available":["2023-06-09T07:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://academicworks.cuny.edu/gc_etds/4869"],"dc:subject":["Data Science","Educational Assessment, Evaluation, and Research","Educational Methods","machine learning","math proficiency","multinominal logistic regression","SPSS Modeler"],"dc:title":["A Comparison of Machine Learning Techniques for Validating Students’ Proficiency in Mathematics"],"thesis:degree_discipline":["Data Analysis & Visualization"],"thesis:degree_level":["Master"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["The Graduate School and University Center of The City University of New York"]},"updated_at":"2026-07-24T01:59:14Z"}