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The longitudinal trajectories of online engagement over a full program
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID. Univ Eastern Finland, Sch Comp, Joensuu Campus,Yliopistokatu 2,POB 111, FI-80100 Joensuu, Finland..ORCID iD: 0000-0001-5881-3109
Univ Politecn Madrid, ETSI Telecomunicac, Dept Ingn Sistemas Telemat, Avda Complutense 30, Madrid 28040, Spain..ORCID iD: 0000-0002-9621-1392
2021 (English)In: Computers and education, ISSN 0360-1315, E-ISSN 1873-782X, Vol. 175, article id 104325Article in journal (Refereed) Published
Abstract [en]

Student engagement has a trajectory (a timeline) that unfolds over time and can be shaped by different factors including learners' motivation, school conditions, and the nature of learning tasks. Such factors may result in either a stable, declining or fluctuating engagement trajectory. While research on online engagement is abundant, most authors have examined student engagement in a single course or two. Little research has been devoted to studying online longitudinal engagement, i.e., the evolution of student engagement over a full educational program. This learning analytics study examines the engagement states (sequences, successions, stability, and transitions) of 106 students in 1396 course enrollments over a full program. All data of students enrolled in the academic year 2014-2015, and their subsequent data in 2015-2016, 2016-2017, and 2017-2018 (15 courses) were collected. The engagement states were clustered using Hidden Markov Models (HMM) to uncover the hidden engagement trajectories which resulted in a mostly-engaged (33% of students), an intermediate (39.6%), and a troubled (27.4%) trajectory. The mostly-engaged trajectory was stable with infrequent changes, scored the highest, and was less likely to drop out. The troubled trajectory showed early disengagement, frequent dropouts and scored the lowest grades. The results of our study show how to identify early program disengagement (activities within the third decile) and when students may drop out (first year and early second year).

Place, publisher, year, edition, pages
Elsevier BV , 2021. Vol. 175, article id 104325
Keywords [en]
Longitudinal engagement, Trajectories of engagement, Learning analytics, Sequence mining, Survival analysis
National Category
Pedagogy
Identifiers
URN: urn:nbn:se:kth:diva-303949DOI: 10.1016/j.compedu.2021.104325ISI: 000703569700006Scopus ID: 2-s2.0-85114842416OAI: oai:DiVA.org:kth-303949DiVA, id: diva2:1605604
Note

QC 20211025

Available from: 2021-10-25 Created: 2021-10-25 Last updated: 2022-06-25Bibliographically approved

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