Time-on-Task Metrics for Predicting Performance

Abstract

Time-on-task is one key contributor to learning. However, how time-on-task is measured often varies, and is limited by the available data. In this work, we study two different time-on-task metrics---derived from programming process data---for predicting performance in an introductory programming course. The first metric, coarse-grained time-on-task, is based on students' submissions to programming assignments; the second, fine-grained time-on-task, is based on the keystrokes that students take while constructing their programs. Both types of time-on-task metrics have been used in prior work, and are supposedly designed to measure the same underlying feature: time-on-task. However, previous work has found that the correlation between these two metrics is not as high as one might expect. We build on that work by analyzing how well the two metrics work for predicting students' performance in an introductory programming course. Our results suggest that the correlation between the fine-grained time-on-task metric and both weekly exercise points and exam points is higher than the correlation between the coarse-grained time-on-task metric and weekly exercise points and exam points. Furthermore, we show that the fine-grained time-on-task metric is a better predictor of students' future success in the course exam than the coarse-grained time-on-task metric. We thus propose that future work utilizing time-on-task as a predictor of performance should use as fine-grained data as possible to measure time-on-task if such data is available.