Time-on-task estimation for tasks lasting hours spread over multiple days.

The ability of a human to retrospectively estimate the amount of time spent on a task is largely only understood when the period of time is seconds- or minutes-long. The lack of research into estimation of longer periods of time can be attributed, in part, to the difficulty of measuring ground truth durations when the task is broken up by other activities in a natural, day-to-day setting. An empirically based model of engagement was recently proposed that statistically estimates time-on-task for computer programming assignments in an introductory computer programming course. Computer programming assignments can be completed in many sessions across days or weeks and, based on recorded keystroke data, an objective ground truth of task duration can be measured. In this work, we take advantage of this new measurement method to explore duration estimation of tasks lasting hours that are spread out over multiple days in a natural setting. Subjects in our study overestimated time-on-task 78% of the time and reported a median of 1.45 hours worked for every actual hour spent on task. We find that self-reports are more accurate when students score higher on their assignments in our data, suggesting the accuracy of estimated time is correlated with task performance.