By presenting each major with egocentric botanic-tree visualization, we can easily compare the 18 majors based on their appearances. Tree features are mapped to different attributes of the data to visualize different aspects of data:
- Trunk size: Total number of students over the three timestamps
- Trunk side: Right: Students who have never changed to another major. Left: otherwise
- Branch: ACT scores
- Branch side: Gender
- Stick and Leaves: Each stick contains students who share the same interest, gender, IMFIT, ACT over the three timestamps. Each leaf represents a group of students in one timestamp.
- Leaf Color: IMFIT value
- Leaf Size: Number of students
- Fruit: Major matches with “HighestLevel2”
- Roots: Total number of students in each timestamp separated by gender
- The trees are sorted by student counts so it is straightforward to identify which are the most/least popular majors.
- ACT score 33-36 students tend not to choose those less popular majors.
- In “Health Admin. & Assisting”, most students who never changed majors are female as in contrast to “Repair, Production, & Construction” and “Eng. Tech. & Drafting” where most students remained the same major are male.
- “Area, Eth. & Multidiscip. Studies“ has the most unbalanced tree while very few students stayed in this major. Moreover, only one fruit on the left side of the trunk means that even fewer students’ interest best fits this major.
- “Business”, “Sciences-Biological & Physical”, and “Health Sci. & Techno.” have relatively more students who found good fit than other majors as there are more large green leaves.
- In “Arts-Visual & Performing”, more students who found it a good fit stay.
- From the roots, it is easy to identify the male-dominant majors are “Engineering”, “Comp. Sci. & Mathematics”, “Eng. Tech. & Drafting” and “Repair, Production, & Construction” while female- dominant majors are “Health Sci. & Techno.”, “Education”, “English & Foreign Lang”, and “Health Admin. & Assisting”.
- “Health Admin. & Assisting”, “Health Sci. & Techno.” and “Architecture” have the most dramatic drop out rates after the first timestamp.
Due to space limit, we leave out other findings.
Tsailing Fung, Jia-Kai Chou, Kwan-Liu Ma
email@example.com, firstname.lastname@example.org, email@example.com
University of California, Davis
1 Shields Ave, Davis, CA 9561