Here is our final report for the end of the project. With this project, we were able to generate insights into how a specific professor would be able to improve his courses (he taught 3 courses related to embedded systems at our university).
We felt that there were many ways of breaking a class down into subcomponents that professors would care about, like the labs, lectures, recitations, etc.
Here is an example of the output that we get from running our program:
This first graph here above shows some examples of some keywords that students use to describe a class, and we felt that this information would be useful because it would allow professors to see how students view certain aspects of the course.
This second graph here above shows how students felt about the class as a whole. It factors in the scores for all of the keywords used to describe the class in the first figure above, and basically maps that onto a line graph so that professors can see how their course has evolved as a whole over a period of time.
This third figure here above shows the different categories that we were talking about before. You have a graph, broken up into 9 different subgraphs, each with a main category that corresponds to a part of a class, like the lectures, recitation, projects, etc. Within these subgraphs, you also have different lines showing different aspects of these subcategories. For example, in the lectures catgory, you have different aspects of that category relating to the lecture, like the teaching style, material and pacing for the lecture.
Below is our report of our progress throughout the entire year. This will give you an idea as to why we approached this problem, and how we decided to tackle it.
SOL_Final_Report.pdf