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Cited by 26Online Learning and AnalyticsOnline and Blended LearningIntelligent Tutoring Systems and Adaptive Learning

When, how and for whom changes in engagement happen: A transition analysis of instructional variables

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Saqr, Mohammed, López-Pernas, Sonsoles, Vogelsmeier, Leonie V.D.E.·2023·Computers & Education
DOI 10.1016/j.compedu.2023.104934
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Abstract

The pace of our knowledge on online engagement has not been at par with our need to understand the temporal dynamics of online engagement, the transitions between engagement states, and the factors that influence a student being persistently engaged, transitioning to disengagement, or catching up and transitioning to an engaged state. Our study addresses such a gap and investigates how engagement evolves or changes over time, using a person-centered approach to identify for whom the changes happen and when. We take advantage of a novel and innovative multistate Markov model to identify what variables influence such transitions and with what magnitude, i.e., to answer the why. We use a large data set of 1428 enrollments in six courses (238 students). The findings show that online engagement changes differently —across students— and at different magnitudes —according to different instructional variables and previous engagement states. Cognitively engaging instructions helped cognitively engaged students stay engaged while negatively affecting disengaged students. Lectures —a resource that requires less mental energy— helped improve disengaged students. Such differential effects point to the different ways interventions can be applied to different groups, and how different groups may be supported. A balanced, carefully tailored approach is needed to design, intervene, or support students' engagement that takes into account the diversity of engagement states as well as the varied…

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  • Iterative reviews of LA data and subsequent adjustments in teaching strategies were shown to enhance learning outcomes over time.

    “For instance, iterative reviews of LA data and subsequent adjustments in teaching strategies were shown to enhance learning outcomes over time (Rienties et al., 2018;Saqr et al., 2023).”

    From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Saqr et al., 2023

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Authors
Saqr, M., López-Pernas, S., & Vogelsmeier, L. V. D. E.
Affiliations
  • University of Eastern Finland (FI)
  • Tilburg University (NL)
Corresponding author
Saqr, Mohammed
ORCID
Venue
Computers & Education
Publisher
Elsevier BV
Published
2023-12
Volume
207
Pages
104934
ISSN
0360-1315 (ISSN-L)
DOI
10.1016/j.compedu.2023.104934
OpenAlex
W4386969528
Cited by
26
References
13
Citations by year
2026: 10 · 2025: 10 · 2024: 5 · 2023: 1
FWCI
5.93
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers & Education (publishedVersion, cc-by)
  • Research portal (Tilburg University) (publishedVersion, cc-by)
Funders
Academy of Finland (350560)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Education › Online and Blended Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
online student engagement, disengagement, multi-state Markov model, instructional variables, lectures, student persistence, intervention targeting

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