According to the students’ self rating of their skills, I made the following visualization for each one of them. I tried to place the skills in a logical array around the polygon with the most relevant skills close to each other, in order to have as far as possible a smooth distribution and render the “skill areas” more apparent to the viewer. I also made sure that the diagram scale has a 0 minimum and a 10 maximum for all cases.
After that, I tried to classify the students into according the field, that each one is stronger. This first rough classification was the following.
Programing: Students 4, 6, 14, 22, 24, 27
Artistic: Students 1, 9, 10, 11, 16, 20
Math/Statistics: Students 3, 5, 21, 25
HCI: Students 12, 15, 19, 23, 17, 26
Neutral: Students 2, 7, 8, 13, 18
By observing the above polygons I tried to match the students into groups of 3 in a way that each one’s skills complete the others’. These are the groupings I made based on that observations.
The goal I wanted to achieve through these visualizations was to form groups that are interdisciplinary, which in my view is a strong advantage of a team as it broadens its capabilities. On the other hand I wanted the group members to share common interests because I think it would reinforce the team’s unity and a friendly atmosphere of collaboration, which is a very important factor in a team’s success as well.
As we can see above, in most cases the polygons tend to complete each other, as in areas of small cavities of a student's polygon another student's of the same group attempts to cover it with a respective peak.
With regards to my second criterion I transcribed the students' answers into labeled fields of interest. Then I made some bar graphs displaying these interests in a descending order of popularity. The different colors represent the different students.
Unfortunately, I didn’t find a way to combine my two criteria because the skills and the interests seemed in most cases to coincide. This means that one student’s skills are similar to their interests, which makes it difficult to combine the interdisciplinarity with the common interests.
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| Students' skills |
After that, I tried to classify the students into according the field, that each one is stronger. This first rough classification was the following.
Programing: Students 4, 6, 14, 22, 24, 27
Artistic: Students 1, 9, 10, 11, 16, 20
Math/Statistics: Students 3, 5, 21, 25
HCI: Students 12, 15, 19, 23, 17, 26
Neutral: Students 2, 7, 8, 13, 18
By observing the above polygons I tried to match the students into groups of 3 in a way that each one’s skills complete the others’. These are the groupings I made based on that observations.
The goal I wanted to achieve through these visualizations was to form groups that are interdisciplinary, which in my view is a strong advantage of a team as it broadens its capabilities. On the other hand I wanted the group members to share common interests because I think it would reinforce the team’s unity and a friendly atmosphere of collaboration, which is a very important factor in a team’s success as well.
 |
| Groups' skills |
As we can see above, in most cases the polygons tend to complete each other, as in areas of small cavities of a student's polygon another student's of the same group attempts to cover it with a respective peak.
With regards to my second criterion I transcribed the students' answers into labeled fields of interest. Then I made some bar graphs displaying these interests in a descending order of popularity. The different colors represent the different students.
 |
| Students' interests |
Unfortunately, I didn’t find a way to combine my two criteria because the skills and the interests seemed in most cases to coincide. This means that one student’s skills are similar to their interests, which makes it difficult to combine the interdisciplinarity with the common interests.
