Towards a fuller picture: Triangulation and integration of the measurement of self‐regulated learning based on trace and think aloud data.
Background: Many learners struggle to productively self‐regulate their learning. To support the learners' self‐regulated learning (SRL) and boost their achievement, it is essential to understand the cognitive and metacognitive processes that underlie SRL. To measure these processes, contemporary SRL...
| Publicado en: | Journal of Computer Assisted Learning Vol. 39; no. 4; pp. 1303 - 1325 |
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| Autores principales: | , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2023
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| 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=164914326&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164914326 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: 164914326 162224896 164914326 164914326 10.1111/jcal.12801 164914326 ppf: 1303 ppct: 22 formats: tig: atl: Towards a fuller picture: Triangulation and integration of the measurement of self‐regulated learning based on trace and think aloud data. aug: au: Fan, Yizhou Rakovic, Mladen van der Graaf, Joep Lim, Lyn Singh, Shaveen Moore, Johanna Molenaar, Inge Bannert, Maria Gašević, Dragan affil: Graduate School of Education, Peking University, Beijing, China sug: subj: Self-Directed Learning Data Mining Cognition Students, College Self Regulation Learning Methods Human Male Female Young Adult Task Performance and Analysis Learning Environment Descriptive Statistics Data Analysis Software Descriptive Research Inferential Statistics Friedman Test Wilcoxon Signed Rank Test Post Hoc Analysis Male Female ab: Background: Many learners struggle to productively self‐regulate their learning. To support the learners' self‐regulated learning (SRL) and boost their achievement, it is essential to understand the cognitive and metacognitive processes that underlie SRL. To measure these processes, contemporary SRL researchers have largely utilized think aloud or trace data, however, not without challenges. Objectives: In this paper, we present the findings of a study that investigated how concurrent analysis and integration of think aloud and trace data could advance the measurement of SRL and assist in better understanding the mechanisms of SRL processes, especially those details that remain obscured by observing each data channel individually. Methods: We concurrently collected think aloud and trace data generated by 44 university students in a laboratory setting and analysed those data relative to the same timeline. Results: We found that the two data channels could be interchangeably used to measure SRL processes for only 17.18% of all the time segments identified in a learning task. Moreover, SRL processes for around 45% of all the time segments could be detected via either trace data or think aloud data. For another 27.17% of all the time segments, different SRL processes were detected in both data channels. Conclusions: Our results largely suggest that the two data collection methods can be used to complement each other in measuring SRL. In particular, we found that think aloud and trace data could provide different perspectives on SRL. The integration of the two methods further allowed us to reveal a more complex and more comprehensive temporal associations among SRL processes compared to using a single data collection method. In future research, the integrated measurement of SRL can be used to improve the detection of SRL processes and provide a fuller picture of SRL. Lay Description: What is already known about this topic: To support the learners' self‐regulated learning (SRL) and boost their achievement, it is essential to understand the cognitive and metacognitive processes that underlie SRL.To measure SRL processes, contemporary researchers have largely utilized think aloud or trace data, however, not without challenges.Think aloud and trace data should complement each other and, when analysed concurrently, can provide a more valid and fuller picture of SRL than when analysed separately, which is typically the case in the SRL research to date. What this paper adds: We proposed a novel alignment approach to triangulate the measurement of self‐regulated learning (SRL) based on trace and think aloud data.We defined integration rules to integrate the measurement results from trace data and think aloud, which helped to provide a fuller picture of SRL.The integrated results revealed a more complex, complete and comprehensive SRL process map compared to using a single method. Implications for practice and/or policy: First, our results suggest that using a single measurement method can often reveal SRL processes only partially.Second, our findings indicated that the integration of the two measurement methods could not address all their methodological shortcomings and more research is needed towards new integrative approaches that can further reduce the number of misaligned results.In future research, the integrated measurement of SRL can be used to improve the detection of SRL processes and provide a fuller picture of SRL.More specifically, the integrated measurement of SRL can be used in the future to better (1) test the effects of instructional SRL interventions, for example, scaffolding; and (2) evaluate how learners use specific learning tools. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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