Abstract
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 |
| Pages | 26-35 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781450362566 |
| DOIs | |
| 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 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Conference
| Conference | 9th International Conference on Learning Analytics and Knowledge, LAK 2019 |
|---|---|
| Country | United States |
| City | Tempe |
| Period | 3/4/19 → 3/8/19 |
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Keywords
- Academic performance
- Course sequencing
- Course timing
- Curriculum planning
- Degree planning
- Degree similarity
- GPA
- Time to degree
- Time to degree prediction
- Undergraduate education
Cite this
A study on curriculum planning and its relationship with graduation GPA and time to degree. / Morsy, Sara; Karypis, George.
Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019. Association for Computing Machinery, 2019. p. 26-35 (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A study on curriculum planning and its relationship with graduation GPA and time to degree
AU - Morsy, Sara
AU - Karypis, George
PY - 2019/3/4
Y1 - 2019/3/4
N2 - 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.
AB - 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.
KW - Academic performance
KW - Course sequencing
KW - Course timing
KW - Curriculum planning
KW - Degree planning
KW - Degree similarity
KW - GPA
KW - Time to degree
KW - Time to degree prediction
KW - Undergraduate education
UR - http://www.scopus.com/inward/record.url?scp=85062779080&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062779080&partnerID=8YFLogxK
U2 - 10.1145/3303772.3303783
DO - 10.1145/3303772.3303783
M3 - Conference contribution
AN - SCOPUS:85062779080
T3 - ACM International Conference Proceeding Series
SP - 26
EP - 35
BT - Proceedings of the 9th International Conference on Learning Analytics and Knowledge
PB - Association for Computing Machinery
ER -