Penetrating the black box of time-on-task estimation

Vitomir Kovanović, Dragan Gašević, Shane Dawson, Srećko Joksimović, Ryan S. Baker, Marek Hatala

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

55 Citations (Scopus)

Abstract

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 2015
Subtitle of host publicationFifth International Conference on Learning Analytics and Knowledge 21—20 March 2015, Poughkeepsie, NY, USA
EditorsPaulo Blikstein, Agathe Merceron, George Siemens
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages184-193
Number of pages10
ISBN (Electronic)9781450334174
DOIs
Publication statusPublished - 16 Mar 2015
Externally publishedYes
EventInternational Learning Analytics & Knowledge Conference 2015: Scaling Up: Big Data to Big Impact - Marist College, Poughkeepsie, United States of America
Duration: 16 Mar 201520 Mar 2015
Conference number: 5th
https://web.archive.org/web/20150328092339/http://lak15.solaresearch.org/home

Conference

ConferenceInternational Learning Analytics & Knowledge Conference 2015
Abbreviated titleLAK 2015
Country/TerritoryUnited States of America
CityPoughkeepsie
Period16/03/1520/03/15
Internet address

Keywords

  • Higher Education
  • Learning Analytics
  • Learning Management Systems (LMS)
  • Measurement
  • Moodle
  • Time on task

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