Diving into Students' Transcripts: High School Course-Taking Sequences and Postsecondary Outcomes
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| Title: | Diving into Students' Transcripts: High School Course-Taking Sequences and Postsecondary Outcomes |
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| Language: | English |
| Authors: | Burhan Ogut (ORCID |
| Source: | Grantee Submission. 2023. |
| Peer Reviewed: | Y |
| Page Count: | 29 |
| Publication Date: | 2023 |
| Sponsoring Agency: | Institute of Education Sciences (ED) |
| Contract Number: | R305A190073 |
| Document Type: | Reports - Research |
| Education Level: | High Schools Secondary Education Higher Education Postsecondary Education |
| Descriptors: | High School Students, Course Selection (Students), Correlation, College Attendance, Mathematics Education, English, Language Arts, Science Education, Student Records, Classification, Student Characteristics |
| DOI: | 10.1111/emip.12554 |
| Abstract: | The purpose of this study was to explore high school course-taking sequences and their relationship to college enrollment. Specifically, we implemented sequence analysis to discover common course-taking trajectories in math, science, and English language arts using high school transcript data from a recent nationally representative survey. Through sequence clustering, we reduced the complexity of the sequences and examined representative course-taking sequences. Classification tree, random forests, and multinomial logistic regression analyses were used to explore the relationship between the course sequences students complete and their postsecondary outcomes. Results showed that distinct representative course-taking sequences can be identified for all students as well as student subgroups. More advanced and complex course-taking sequences were associated with postsecondary enrollment. [This paper was published in "Educational Measurement: Issues and Practice" v42 n2 2023.] |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2024 |
| Accession Number: | ED653225 |
| Database: | ERIC |
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| Abstract: | The purpose of this study was to explore high school course-taking sequences and their relationship to college enrollment. Specifically, we implemented sequence analysis to discover common course-taking trajectories in math, science, and English language arts using high school transcript data from a recent nationally representative survey. Through sequence clustering, we reduced the complexity of the sequences and examined representative course-taking sequences. Classification tree, random forests, and multinomial logistic regression analyses were used to explore the relationship between the course sequences students complete and their postsecondary outcomes. Results showed that distinct representative course-taking sequences can be identified for all students as well as student subgroups. More advanced and complex course-taking sequences were associated with postsecondary enrollment. [This paper was published in "Educational Measurement: Issues and Practice" v42 n2 2023.] |
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| DOI: | 10.1111/emip.12554 |
