Penetrating the Black Box of Time-on-task Estimation

Vitomir Kovanovic, Dragan Gasevic, Shane Dawson, Srecko Joksimovic, Marek Hatala, Ryan Baker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

All forms of learning take time. There is a large body of research suggesting that the amount of time spent on learning can improve the quality of learning, as represented by academic performance. The wide-spread adoption of learning technologies such as learning management systems (LMSs), has resulted in large amounts of data about student learning being readily accessible to educational researchers. One common use of this data is to measure time that students have spent on different learning tasks (i.e., time-on-task). Given that LMS systems typically only capture times when students executed various actions, time-on-task measures are estimated based on the recorded trace data. LMS trace data has been extensively used in many studies in the field of learning analytics, yet the problem of time-on-task estimation is rarely described in detail and the consequences that it entails are not fully examined.

This paper presents the results of a study that examined the effects of different time-on-task estimation methods on the results of commonly adopted analytical models. The primary goal of this paper is to raise awareness of the issue of accuracy and appropriateness surrounding time-estimation within the broader learning analytics community, and to initiate a debate about the challenges of this process. Furthermore, the paper provides an overview of time-on-task estimation methods in educational and related research fields.
Original languageEnglish
Title of host publicationLAK '15 Proceedings of the Fifth International Conference on Learning Analytics And Knowledge
Place of PublicationNew York, NY, USA
PublisherACM Press
Number of pages10
ISBN (Electronic)978-1-4503-3417-4
Publication statusPublished - 20 Mar 2015

Keywords / Materials (for Non-textual outputs)

  • Time on task
  • measurement
  • learning analytics
  • higher educaton
  • learning management systems
  • Moodle


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