Profiles in self-regulated learning and their correlates for online and blended learning students

被引:69
|
作者
Broadbent, Jaclyn [1 ,2 ]
Fuller-Tyszkiewicz, Matthew [1 ]
机构
[1] Deakin Univ, Sch Psychol, Geelong, Vic, Australia
[2] Deakin Univ, Ctr Res Assessment & Digital Learning, Geelong, Vic, Australia
关键词
Self-regulated learning strategies; Motivated self-regulation; Online learning; Higher education; Latent profile analysis (LPA); ACADEMIC-PERFORMANCE; UNIVERSITY-STUDENTS; PRIOR KNOWLEDGE; STRATEGY USE; EFFICACY; MOTIVATION; ACHIEVEMENT; HYPERMEDIA; COMPONENTS; OUTCOMES;
D O I
10.1007/s11423-018-9595-9
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
This study examines a person-centered approach to self-regulated learning among 606 University students (140 online, and 466 in blended learning mode). Latent profile analysis revealed five distinct profiles of self-regulated learning: minimal regulators, restrained regulators, calm self-reliant capable regulators, anxious capable collaborators, and super regulators. These profiles showed that: (1) differences in academic success are associated with a learner's capacity for motivational regulation and self-regulated learning strategy implementation, (2) online learners are more likely to belong to profiles that are more adaptive, and less reliant on collaborations with others, (3) for learners at the lower end of the self-regulation spectrum, an increase in both motivational regulation and adoption of self-regulated learning strategies may be academically beneficial, and (4) high motivational regulation and strategy adoption can be all for naught, if the student is also highly anxious with worry and concern regarding performance.
引用
收藏
页码:1435 / 1455
页数:21
相关论文
共 50 条
  • [21] Self-regulated learning in professional students
    Singh, Tara A.
    [J]. CLINICAL TEACHER, 2018, 15 (06): : 513 - 514
  • [22] Supporting students’ self-regulated learning in online learning using artificial intelligence applications
    Sung-Hee Jin
    Kowoon Im
    Mina Yoo
    Ido Roll
    Kyoungwon Seo
    [J]. International Journal of Educational Technology in Higher Education, 20
  • [23] Supporting students' self-regulated learning in online learning using artificial intelligence applications
    Jin, Sung-Hee
    Im, Kowoon
    Yoo, Mina
    Roll, Ido
    Seo, Kyoungwon
    [J]. INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION, 2023, 20 (01)
  • [24] Online Accelerated Learning Experiences and Self-Regulated Learning Skills Among Undergraduate Students
    Yen, Cherng-Jyh
    Ozkeskin, Emrah Emre
    Tankari, Moussa
    Tu, Chih-Hsiung
    Harati, Hoda
    Sujo-Montes, Laura E.
    [J]. INTERNATIONAL JOURNAL OF ONLINE PEDAGOGY AND COURSE DESIGN, 2021, 11 (03) : 17 - 35
  • [25] Blended Learning Integrated with Innovative Learning Strategy to Improve Self-Regulated Learning
    Bahri, Arsad
    Idris, Irma Suryani
    Muis, Hasmunarti
    Arifuddin, Muh
    Fikri, Muh Jibran Nidhal
    [J]. INTERNATIONAL JOURNAL OF INSTRUCTION, 2021, 14 (01) : 779 - 794
  • [27] A Meta-Analysis of Self-Regulated Learning Interventions Studies on Learning Outcomes in Online and Blended Environments
    Guntur, Mochamad
    Purnomo, Yoppy Wahyu
    [J]. ONLINE LEARNING, 2024, 28 (03): : 563 - 584
  • [28] Systematic Review of Self-Regulated Learning with Blended Learning in Digital Space
    Faathima Fayaza, M.S.
    Ahangama, Supunmali
    [J]. IEEE Access, 2024, 12 : 143090 - 143105
  • [29] Exploring the Effects of Web-Enabled Self-Regulated Learning and Online Class Frequency on Students' Computing Skills in Blended Learning Courses
    Shen, Pei-Di
    Tsai, Chia-Wen
    [J]. INTERNATIONAL JOURNAL OF MOBILE AND BLENDED LEARNING, 2009, 1 (03) : 1 - 16
  • [30] Self-regulated learning in online learning environments: strategies for remote learning
    Carter Jr, Richard Allen
    Rice, Mary
    Yang, Sohyun
    Jackson, Haidee A.
    [J]. INFORMATION AND LEARNING SCIENCES, 2020, 121 (5-6) : 321 - 329