Enabling Precision Education by Learning Analytics Applying Trace, Survey and Assessment Data

Dirk Tempelaar, Bart Rienties, Quan Nguyen

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

Abstract

Accurate and timely measurement of learning engagement is crucial for the application of precision education. At the same time, it is still a central research theme, both in the learning analytics community as in the broader area of educational research. 'Engagement is one of the hottest research topics in the field of educational psychology' is for a good reason the opening sentence of a recent special issue. In our contribution, we propose a holistic approach to the measurement of engagement by integrating data of behavioral type through traces of learning processes captured from log files into affective, behavioral, and cognitive measures of engagement collected with surveys and cognitive measures from assessments for and as learning. We apply this holistic approach in an empirical analysis of dispositional learning analytics. Starting from four different engagement profiles created by two-step clustering, we find that these profiles primarily differ in their timing of engagement with learning. Next, we develop regression-based prediction models that make clear that trace, survey, and assessment data have complementary roles in signaling students at risk for failure and are all three crucial constituents of prediction equations that differ in the timing of learning feedback.
Original languageEnglish
Title of host publication2021 International Conference on Advanced Learning Technologies (ICALT)
PublisherIEEE
Pages355-359
Number of pages5
ISBN (Print)978-1-6654-3116-3
DOIs
Publication statusPublished - 15 Jul 2021
Event2021 International Conference on Advanced Learning Technologies (ICALT) - Tartu, Estonia
Duration: 12 Jul 202115 Jul 2021
https://www.computer.org/csdl/proceedings/icalt/2021/1vJZSCaC4SI

Conference

Conference2021 International Conference on Advanced Learning Technologies (ICALT)
Country/TerritoryEstonia
CityTartu
Period12/07/2115/07/21
Internet address

Keywords

  • Education
  • Psychology
  • Predictive models
  • Data models
  • Timing
  • Mathematical model

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