Topic Level Visualization of Student Enrollment Records in a Computer Science Curriculum
Reviewing academic curricula requires a significant investment of time and expertise. Beyond accreditation, curriculum may be reviewed in part or in whole during other administrative efforts including the consideration of new elective courses, faculty-student advising, admission of transfer students...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Education Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7102/15/5/614 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850126924815269888 |
|---|---|
| author | Eliot Bethke Jennifer R. Amos |
| author_facet | Eliot Bethke Jennifer R. Amos |
| author_sort | Eliot Bethke |
| collection | DOAJ |
| description | Reviewing academic curricula requires a significant investment of time and expertise. Beyond accreditation, curriculum may be reviewed in part or in whole during other administrative efforts including the consideration of new elective courses, faculty-student advising, admission of transfer students, internal audits, and more. These activities often require multiple people with deep knowledge of the coursework as well as the discipline(s) involved to pour over scattered documentation and comparatively limited assessment data in order to make an informed decision. In this work, we explored the development of a semi-automated computational approach to visualize a curriculum as described in official course listings at a topic level of detail. We show how our approach can help provide a detailed view of how topics are covered across multiple courses and how these visualizations can show similarities and differences for individual student registration records, paving the way for personalized student support. We also identified opportunities for improvement in this method, including the need to develop more robust topic mapping techniques for short texts. |
| format | Article |
| id | doaj-art-9b6c294c81d147fbb5b4a0c94d90c877 |
| institution | OA Journals |
| issn | 2227-7102 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj-art-9b6c294c81d147fbb5b4a0c94d90c8772025-08-20T02:33:48ZengMDPI AGEducation Sciences2227-71022025-05-0115561410.3390/educsci15050614Topic Level Visualization of Student Enrollment Records in a Computer Science CurriculumEliot Bethke0Jennifer R. Amos1Department of Bioengineering, Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USADepartment of Bioengineering, Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USAReviewing academic curricula requires a significant investment of time and expertise. Beyond accreditation, curriculum may be reviewed in part or in whole during other administrative efforts including the consideration of new elective courses, faculty-student advising, admission of transfer students, internal audits, and more. These activities often require multiple people with deep knowledge of the coursework as well as the discipline(s) involved to pour over scattered documentation and comparatively limited assessment data in order to make an informed decision. In this work, we explored the development of a semi-automated computational approach to visualize a curriculum as described in official course listings at a topic level of detail. We show how our approach can help provide a detailed view of how topics are covered across multiple courses and how these visualizations can show similarities and differences for individual student registration records, paving the way for personalized student support. We also identified opportunities for improvement in this method, including the need to develop more robust topic mapping techniques for short texts.https://www.mdpi.com/2227-7102/15/5/614curriculum visualizationnatural language processing (NLP)student outcomeshigher educationcomputer science |
| spellingShingle | Eliot Bethke Jennifer R. Amos Topic Level Visualization of Student Enrollment Records in a Computer Science Curriculum Education Sciences curriculum visualization natural language processing (NLP) student outcomes higher education computer science |
| title | Topic Level Visualization of Student Enrollment Records in a Computer Science Curriculum |
| title_full | Topic Level Visualization of Student Enrollment Records in a Computer Science Curriculum |
| title_fullStr | Topic Level Visualization of Student Enrollment Records in a Computer Science Curriculum |
| title_full_unstemmed | Topic Level Visualization of Student Enrollment Records in a Computer Science Curriculum |
| title_short | Topic Level Visualization of Student Enrollment Records in a Computer Science Curriculum |
| title_sort | topic level visualization of student enrollment records in a computer science curriculum |
| topic | curriculum visualization natural language processing (NLP) student outcomes higher education computer science |
| url | https://www.mdpi.com/2227-7102/15/5/614 |
| work_keys_str_mv | AT eliotbethke topiclevelvisualizationofstudentenrollmentrecordsinacomputersciencecurriculum AT jenniferramos topiclevelvisualizationofstudentenrollmentrecordsinacomputersciencecurriculum |