WPSS: dropout prediction for MOOCs using course progress normalization and subset selection

Abstract

There are existing multi-MOOC level dropout prediction research in which many MOOCs' data are involved. This generated good results, but there are two potential problems. On one hand, it is inappropriate to use which week students are in to select training data because courses are with different durations. On the other hand, using all other existing data can be computationally expensive and inapplicable in practice.

To solve these problems, we propose a model called WPSS (<u>WP</u>ercent and <u>S</u>ubset <u>S</u>election) which combines the course progress normalization parameter wpercent and subset selection. 10 MOOCs offered by The University of Hong Kong are involved and experiments are in the multi-MOOC level. The best performance of WPSS is obtained in neural network when 50% of training data is selected (average AUC of 0.9334). Average AUC is 0.8833 for traditional model without wpercent and subset selection in the same dataset.