Unfolding self‐regulated learning profiles of students: A longitudinal study.
Background: It is vital to understand students' Self‐Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable‐oriented approach. Moreover, little has been k...
| Publicado en: | Journal of Computer Assisted Learning Vol. 39; no. 4; pp. 1116 - 1132 |
|---|---|
| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164914335&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164914335 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2023 vid: 39 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 164914335 164124322 164914335 164914335 10.1111/jcal.12830 164914335 ppf: 1116 ppct: 16 formats: tig: atl: Unfolding self‐regulated learning profiles of students: A longitudinal study. aug: au: Esnaashari, Shadi Gardner, Lesley A. Arthanari, Tiru S. Rehm, Michael affil: University of Auckland, Auckland, New Zealand sug: subj: Self-Directed Learning Students, College Motivation Human Male Female Adolescence Young Adult Prospective Studies Surveys Self Report Data Analysis Software Descriptive Statistics Analysis of Variance Multivariate Analysis of Variance Questionnaires Feedback Comparative Studies Algorithms Adolescent: 13-18 years Male Female ab: Background: It is vital to understand students' Self‐Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable‐oriented approach. Moreover, little has been known about the unfolding process of students' SRL profiles. Objectives: We present the insights derived from a study that measured motivation and the learning strategies used by 198 students of a university entry‐level, business school, BL course to develop an understanding of students' SRL processes. Methods: The Strategies for Learning Questionnaire (MSLQ) was used to survey 198 students three times during a semester to investigate SRL profiles and how they unfolded as the course progressed using a person‐oriented approach. Through a clustering approach, we focus on MSLQ's motivation aspects as its importance has been emphasised by different SRL theories, and extant research into motivation in learning analytics (LA) is still lacking. Results and Conclusions: Through the longitudinal clustering approach, we identified minimally, average, and highly SRL profiles. We acknowledged that students might change their SRL profiles as the course progressed as a result of feedback they received. What are the 1 or 2 Major Takeaways from the Study?: This study contributes to the SRL theory by examining students' SRL profiles adaptation longitudinally (addressing the challenge identified regarding the cyclical nature of SRL). This study contributes to LA by investigating motivational constructs currently lacking in the field and bringing forward theory based empirical evidence to inform theory and practice. Lay Description: What is currently known about the subject matter: Self‐regulated learning (SRL) is important for academic achievement, especially during online learning; more research is needed to understand students' SRL and its cyclical nature.Learning analytics (LA) is an advocate for gathering and analysing data for supporting students' learning. It lacks empirical evidence based on theoretical and students' motivational studies.Institutions will benefit from more research on evidence‐based practice in Blended Learning (BL). What this paper adds to that: Evidence for the understanding of students' SRL, identifying student subgroups (different SRL profiles), and observing the cyclical nature of SRL during BL courses.We enhanced LA through the use of empirical research into motivation and its mapping of students' SRL profiles.Contribute to knowledge within BL about the dynamics of students' motivation, strategy use, and SRL, and showing that successful students better self‐regulate their learning.Evidence to support the emerging role of LA in identifying at‐risk students during BL.Evidence shows the most important motivational and learning strategy constructs that are significantly correlated with the final score in BL. The implications of study findings for practitioners: Through LA, students, educators, and institutions can benefit when universities implement BL.Enabling educators to better understand their students' SRL and how students can adapt quite different SRL profiles over time. Through this information, they can help students adopt a better profile. They also can help students in their SRL process, for example, by applying appropriate interventions.Pedagogically helps lecturers when developing the instructional and interventional design. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|