In recent years, several data-driven methods have been developed to help undergraduate students during course selection and sequencing. These methods tend to utilize the whole set of past course registration data, regardless of the past students' graduation GPA and time to degree (TTD). Though some previous work has shown through the results of their developed models that students of different GPA tend to take courses in different sequence, the actual analysis of the degree plans and how/if they relate to the students' graduation GPA and time-to-degree has not received much attention. This study analyzes how the student's academic level when they take different courses, as well as the pairwise degree similarity between pairs of students relate to the students' graduation GPA and TTD. Our study uses a large-scale dataset that contains 25 majors from different colleges at the University of Minnesota and spans 16 years. The analysis shows that TTD is highly correlated with both the timing and ordering of courses that students follow in their degree plans, while the correlation between graduation GPA and the course timing and ordering is not as high. We also perform a case study that uses course timing and ordering features to predict whether the student at each semester will graduate on-time or overtime. The results show that careful curriculum planning is needed to improve graduation rates in universities.
|Original language||English (US)|
|Title of host publication||Proceedings of the 9th International Conference on Learning Analytics and Knowledge|
|Subtitle of host publication||Learning Analytics to Promote Inclusion and Success, LAK 2019|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|State||Published - Mar 4 2019|
|Event||9th International Conference on Learning Analytics and Knowledge, LAK 2019 - Tempe, United States|
Duration: Mar 4 2019 → Mar 8 2019
|Name||ACM International Conference Proceeding Series|
|Conference||9th International Conference on Learning Analytics and Knowledge, LAK 2019|
|Period||3/4/19 → 3/8/19|
Bibliographical noteFunding Information:
This work was supported in part by NSF (1447788, 1704074, 1757916, 1834251), Army Research Office (W911NF1810344), Intel Corp, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute, http://www.msi.umn.edu.
© 2019 Association for Computing Machinery.
Copyright 2019 Elsevier B.V., All rights reserved.
- Academic performance
- Course sequencing
- Course timing
- Curriculum planning
- Degree planning
- Degree similarity
- Time to degree
- Time to degree prediction
- Undergraduate education