Reflection – Learning Launch

Challenge

I work as a research engineer in a big university in a developing country. There are around 30  thousands undergraduate students and 3 thousands graduate students in the university. It also  has the largest library of digitalized documents in the region, including thousands of thesis  throughout the years. It turns out that plagiarism becomes a serious concern in this environment.  There are commercial products produced to detect plagiarism. However, these products are too  expensive and they don’t usually work well with the native language. Therefore, our research  group has been tasked to build a plagiarism detection system for the university. The team has  little experiences with such a large-scale system. Several ideas have been proposed regarding  how the system should be built. Some look pretty good on paper but we don’t really know until  we try them out in practice.

Selection

We have decided to use learning launch to test the best idea we have. It’s really an appropriate  option as with this kind of problems, we need real world data input to adjust our assumptions.

Application

We asked for a small number of machines and spent about 2 months building a prototype for the  system. The latter was launched with a subset of the available documents and we restricted  usages to 3 thousands graduate students only. People are encouraged to use the system while  we constantly collected the performance data. We also paied very much attention to human  factors, human experiences in the process. We organized several workshops where we invited  users, stakeholders to gather their feedbacks and demands.

Insight

By applying the learning launch tool, we started to see several drawbacks in our ideas. The system  works well with a reasonable size dataset but when the latter becomes bigger and bigger, the  user experience gets worse and worse. Users waited too long for a detection result to come out.  Some components in our design started to break more requently. It means that our assumptions  regarding how the system handles the scalabilty and the workload need to be revised.

Approach

We consider that the learning launch is a valuable tool, especially in our domain. We will continue  to apply this tool to other projects. The feedbacks it gives us are really helpful. Regarding the  plagiarism detection system we are building, we will take into account all the data we have and  the second launch will focus on testing the system’s scalability.

