1 git pull 2 git checkout iterationi j 3 bundle

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1 git pull 2 git checkout iterationi -j 3 bundle install --without production 4 rake db:migrate db:test:prepare db:seed 5 rake cucumber 6 rake spec (2) CodeClimate Starting in Fall 2013 we ask students to use CodeClimate to check their repos, and we deduct points if they have an unusual number of red flags or yellow flags. 6.6 Managing Student Teams One of the first acts of the teaching assistants was to remind students to contact customers as early as possible, since non-technical customers may not respond immediately. We recommended contacting them a week in advance of the deadline. Some teams admitted that they contacted customers the same day they were supposed to tell us when they set the meeting, and they did not get a response in time. As the first meetings just involved user stories and LoFi UI, we made sure we had covered that material in lecture before the meetings with the customers. Figure 3.1 shows that we covered that material in the sixth lecture in Fall 2012, which placed it at the end of the third week. We split the subsequent Agile iterations into two pieces, and teaching assistants met with each team for 10 minutes every two weeks and checked what they submitted online every week. For each iteration X.1, the students had to pick stories and write Cucumber tests for them. For each iteration X.2, they had to implement the stories using RSpec and TDD and deploy to Heroku. The face-to-face meetings with the teaching assistants mostly ran smoothly, implementing the features that they agreed with the customers. In the meetings, we had the students show off features they had implemented, discuss challenges they had, and identify upcoming features and challenges. Occasionally we’d click around in their app and see exactly what was and wasn’t working. A few times the teaching assistants had to tell teams that their features were not substantial enough for an iteration. The combination of monitoring via Tracker
Armando Fox ([email protected]) 29 and using the app on Heroku made it easy for the teaching assistants to quickly tell which groups were doing relatively less than others, and they let the students know if they were underperforming. The teaching assistants spent about 10 minutes face-to-face with every project every other week meeting with groups, and another 10 minutes per project per week between meetings to test the app. Part of the educational benefit of working in groups is learning to work as a team, and a few teams usually have problems. To try to head the problems off, everyone on the team had to rank each team member with a score from 1 to 10 for each iteration. We then anonymized and shared that feedback from the other teammates each iteration. For the first feedback there was no impact on the grading; it was just a warning. In the rest of the iterations it factored into the grades. Grading gets students’ attention! The actual grading was not just the average point

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