Student engagement profiles in a mobile app: Links to self-regulated learning and performance.

Jacqueline Wong, Mohammad Khalil, Vsevolod Suschevskiy, Martine Baars, Bjorn de Koning, Fred Paas

Abstract read

In one paragraph

Article in Educational technology research and development : ETR & D, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jacqueline WongDepartment of Education, Faculty of Social and Behavioral Sciences, Utrecht University, Utrecht, the Netherlands.ORCID 0000-0002-5387-7696

Mohammad KhalilCentre for the Science of Learning & Technology (SLATE), Faculty of Psychology, University of Bergen, Bergen, Norway.

Vsevolod SuschevskiyNorthwestern University, Illinois, USA.

Martine BaarsBehavioural Science Institute, Radboud University, Nijmegen, the Netherlands.

Bjorn de KoningDepartment of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, the Netherlands.

Fred PaasDepartment of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Engaging in independent learning during self-study time is an essential part of learning in higher education. The ubiquity of mobile devices and their applications offer students a flexible, on-the-go learning experience. However, flexible learning environments require students to self-regulate their learning. The current study examined student engagement with a mobile study app intended for students to engage in retrieval practice with feedback as part of self-regulated learning (SRL). Three research questions were addressed: (1) What are the student engagement profiles that can be identified based on students' activity with the study app? (2) Do the profiles differ in SRL and academic performance? and (3) What is the relationship between the identified student profiles, SRL, and academic performance? Through a learning analytics approach, results identified three distinct student profiles: active, disengaged, and utilitarian. Self-efficacy and time-management positively predicted exam grades, while self-evaluation showed an inverse effect. Active engagement positively influenced exam grades, underscoring individual differences in SRL.

Indexed as

Latent profile analysisMobile applicationsRetrieval practiceSelf-regulated learning (SRL)Student engagementTrace data

Identifiers

PMID42111343

DOI10.1007/s11423-026-10586-2

PMCPMC13152958

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.