In Georgia Tech, an approximately 5500 new international students arrive on campus each year. Good dining is an important part in overall adaptation to a new life abroad, but we have found the following experience widely shared among new international students (including ourselves):
“You are an international student attending Georgia Tech. While visiting the cafeteria you are confused by the many unfamiliar food choices and have difficulty understanding the menu. You cannot name the foods and do not know what foods you would enjoy. You are afraid to ask the server for information about the food because there is a long line and that would inconvenience those behind you.”
We are inspired to find a solution to help international students adapt to dining on campus in a better way.
We carried out four 30-minute observation sessions in different on-campus dining places. We sticked to non-participatory principle and took notes of international student's behavior when they order food.
After natural observations, we need to further learn about our users’ feelings, values and problems when dining on campus. We want to identify how users’ personal traits relate to their problems. Therefore, the team together carried out eight contextual interviews with first-year international students. We recruited students from 5 different contries. We included native as well as non-native English speakers, students who are new to the US as well as students who have been in the US for several years. We randomly assigned participants to three different kinds of dining places on campus-cafeteria, fast food store and buffet.
In a typical session, we observe the participant order at one of the typical dining place on campus that he or she is not familiar with. Then we ask questions about his or her experience when ordering, opinion on the food, and other general questions about attitudes and habits.
An affinity map was created based on the findings from contextual interviews and observations to help the team make better sense of the (unexpectedly tons of) data collected.
Process: We started by reading the notes out loud and sticking it on the wall. We looked out for similar notes to put under the previous notes, or create a new column if no match was found. The blue sticky notes are highlights of their own columns, written in the user's tone of voice. Then, the pink notes were introduced to group the various ideas shown on the blue notes. We then summed up the afficnity map by categorizing the pink notes with green ones.