자료유형  

 학위논문

Control Number  

0016932665

International Standard Book Number  

9798379844523

Dewey Decimal Classification Number  

370

Main Entry-Personal Name  

Karakis, Nesibe.

Publication, Distribution, etc. (Imprint  

[S.l.] : Purdue University., 2021

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2021

Physical Description  

1 online resource(108 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

General Note  

Advisor: Pereira, Nielsen.

Dissertation Note  

Thesis (Ph.D.)--Purdue University, 2021.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약This study investigated high-achieving and non-high-achieving students' persistence in STEM fields using nationally representative data from the High School Longitudinal Study of 2009 for the years 2009, 2012, 2013, 2013-2014, and 2016. The results indicated that approximately 70% of high-achieving and non-high-achieving students continued their initial STEM degrees within 3 years of college enrollment. The study revealed that the most important predictors of STEM persistence were: math proficiency level, school belonging, school engagement, school motivation, school problems, science self-efficacy, credits earned in computer sciences, GPA in STEM courses, credits earned in STEM courses, and credits earned in Advanced Placement/International Baccalaureate (AP/IB) courses. Based on the results, math proficiency was the most important variable in the study for both high-achieving and non-high-achieving students. Even though credits earned in AP/IB combined were among the most important variables, they were two times more important for high-achieving students (6.86% vs. 3.37%). Regarding demographic information related variables, socioeconomic status was the most important variable among gender, ethnicity, and urbanicity in models predicting STEM persistence and had higher importance for non-high-achieving students. Furthermore, Hispanic students' proportion of persistence differed from other underrepresented populations' persistence. Non-high-achieving Hispanic students had the highest persistence rate, similar to well-represented populations (i.e., White, Asian). Machine learning methods used in the study including random forest and artificial neural network provided good accuracy for both achievement groups. Random forest accuracy was over 82% with the Synthetic Minority Over-Sampling Technique (SMOTE) dataset, while artificial neural network accuracy was over 92%.

Subject Added Entry-Topical Term  

Enrollments.

Subject Added Entry-Topical Term  

Mathematics education.

Subject Added Entry-Topical Term  

Socioeconomic factors.

Subject Added Entry-Topical Term  

Science education.

Subject Added Entry-Topical Term  

Neural networks.

Subject Added Entry-Topical Term  

Education.

Subject Added Entry-Topical Term  

Mathematics.

Subject Added Entry-Topical Term  

Sociology.

Added Entry-Corporate Name  

Purdue University.

Host Item Entry  

Dissertations Abstracts International. 85-01B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:643304