I'm using logistic regression to predict student retention in an online course.
I have a data of student interactions within a web platform of an online course. The course spans 6 weeks, with new lecture resources and new assignments uploaded at the beginning of each week. The weekly assignments due at the end of each week. Students can watch lecture videos, view/write forum posts, and do assignments. Students can drop out of the course any time (i.e. no longer interacting within the course platform).
Each week, I want to predict the likelihood of a student staying within the course in the next week (stay in the next week=1, out in the next week=0). The predictors are the number of times the student watch the lecture videos (video_views), the number of posts the student read (posts_read), the number of posts the student wrote (post_written) and the student's score of this week assignment (score).
I'm thinking of building 6 models using logistic regression, for each week. But I also want to make a connection between, say, week 6's model and week 5's model. Can such connection be shown if I use percentage of cumulative values for each predictors (e.g. cumulative number of assignment score out of total assignment score for the whole course) instead of weekly values?
Should I just build a single model with the course week (course_week) as another predictor? This is my first time using logistic regression, and I'm not sure about putting a time variable in a logistic regression model.