There is a common theme in the issues we have identified with this course: it was very ambitious. That makes complete sense for an experimental class: when a professor designs a new course, they have certain concepts they want to teach, and it is difficult to cut that dream down. However, the result was that we covered a lot of information very quickly, with more emphasis on breadth than depth. As a result, I have a vague familiarity with many important issues, but not a true, in-depth understanding of any of them.
This is particularly true during the later portion of the class when we began to talk about statistics, regressions, and the problems that come with them. I recognize that each week we talked about really important issues, and that not being familiar with them could make us sound like idiots. However, we didn't get much practice applying the information we had learned or experiencing the practical implications on it. I think the later part of the semester could benefit from having a more narrow focus, on a few important topics that we could cover in greater depth. This would allow more of the group activities where we discussed a case or fact pattern, or tried to work through a question as a group. Additionally, it would reduce the reading load and time requirement of the course, and would increase our understanding and comfort with the subjects.
One example of this problem was in the coding section of the course. I have mixed feelings about that portion of the class. It was clearly the least law-related portion, and the most significant area where I had absolutely no knowledge at the beginning. It is also clearly necessary for us to do our own visualizations and analyses. However, it was definitely the most time-consuming and frustrating part of the class. I spent an inordinate amount of time on the programming-based problem sets. I recognize that coding is probably like math--once you know how to do think X, it is much easier to figure out how to do thing Y. However, it was very difficult to see the skills we developed early in the semester translate to the coding we did toward the end.
Another example of this is the likelihoods issue. That is a complex topic, that I was completely unfamiliar with. But, to this day, I don't think I understood a word that Professor Sullivan said to us.
There is a common theme in the issues we have identified with this course: it was very ambitious. That makes complete sense for an experimental class: when a professor designs a new course, they have certain concepts they want to teach, and it is difficult to cut that dream down. However, the result was that we covered a lot of information very quickly, with more emphasis on breadth than depth. As a result, I have a vague familiarity with many important issues, but not a true, in-depth understanding of any of them.
This is particularly true during the later portion of the class when we began to talk about statistics, regressions, and the problems that come with them. I recognize that each week we talked about really important issues, and that not being familiar with them could make us sound like idiots. However, we didn't get much practice applying the information we had learned or experiencing the practical implications on it. I think the later part of the semester could benefit from having a more narrow focus, on a few important topics that we could cover in greater depth. This would allow more of the group activities where we discussed a case or fact pattern, or tried to work through a question as a group. Additionally, it would reduce the reading load and time requirement of the course, and would increase our understanding and comfort with the subjects.
One example of this problem was in the coding section of the course. I have mixed feelings about that portion of the class. It was clearly the least law-related portion, and the most significant area where I had absolutely no knowledge at the beginning. It is also clearly necessary for us to do our own visualizations and analyses. However, it was definitely the most time-consuming and frustrating part of the class. I spent an inordinate amount of time on the programming-based problem sets. I recognize that coding is probably like math--once you know how to do think X, it is much easier to figure out how to do thing Y. However, it was very difficult to see the skills we developed early in the semester translate to the coding we did toward the end.
Another example of this is the likelihoods issue. That is a complex topic, that I was completely unfamiliar with. But, to this day, I don't think I understood a word that Professor Sullivan said to us.