These notebooks detail the building of a time series classifier that predicts which students in an online course will fail or drop out, after just a few weeks in to the course. Logistic regression, random forests, and LSTM neural networks are explored.
Download data here: https://analyse.kmi.open.ac.uk/#open-dataset
In order to run the notebooks as they are, save the data in a directory named data
0_example_recommender: building of a basic recommender for study materials that utilizes user similarity weighted by course success.
0_timelines: graphs showing student click behavior for all courses (one graph per course). Here we can see a definite pattern emerge: that the click behavior of students who ultimately fail is more volatile than that of students who ultimately pass.
1_student_logreg: logistic regression classifying students as succeed/fail based on student demographic information
2_lstm_rf_diffs: long short term memory neural network classifying students as succeed/fail based on student click behavior from the first quarter of course timelines (in order to simulate real-world prediction circumstances)
3_timeseries_features_model: the most successful model in this project. Here, a random forest classifier achieves 79% accuracy (and importantly, 87% precision on failing students) when given only the best, most useful features as identified by the tsfresh package, which is designed specifically for time series feature extraction.
proposal.md is the initial proposal for this project, which was originally focused on building a novel study material recommender
presentation.pdf contains the slides used in a presentation on this classification project. Not too informative without the accompanying speech, but good for getting a nutshell overview in 60 seconds.