Charles Darwin O. Godinez^1,, Laila S. Lomibao²

The abrupt shift of learning venue from face-to-face to online distance learning actually instigated many pedagogical problems such as students’ tendencies to procrastinate on tasks and assignments required as learners take on independent and active role. Delayed and unsubmitted tasks then result to poor academic performance. In this paper, a method for the early detection of academic procrastination tendencies in online mathematics learning was explored using educational data mining methods. Thirty-five (35) input features were retrieved from a repository of student data from St. Rita’s College of Balingasag, Philippines. K-modes clustering was used for output label clustering, the Boruta Algorithm for feature selection, and a Gaussian-Bernoulli mixed Naïve Bayes algorithm for model building, all implemented in RStudio. Focus Group Discussions were also performed to triangulate results of the model as well as suggest actions for mathematics’ educators based on students’ experiences. The results revealed that only 14 features out of the 35 identified features are relevant in predicting students’ procrastination tendencies and our Gaussian-Bernoulli Mixed Naïve Bayes model can successfully predict students’ later procrastination behaviors – whether they are Non Procrastinators, Low, Moderate, or High Procrastinators – with a testing accuracy of 89% and Kappa Score of 84%. Mathematics’ educators can utilize this model to forecast and take actions to prevent the adverse effects of students’ procrastination.

academic procrastination, online learning, educational data mining