A latent profile analysis of adult students' online self-regulation in blended learning environments. (October 2019)

• Record Type: Journal Article
• Authors: Vanslambrouck, Silke; Zhu, Chang; Pynoo, Bram; Lombaerts, Koen; Tondeur, Jo; Scherer, Ronny
• Is Part Of: Computers in human behavior. Volume 99(2019)
• Page Start: 126
• Page End: 136
• Publication Date: 2019-10
• Subjects: Achievement motivation -- Adult education -- Blended learning -- Latent profile analysis -- Online self-regulated learning; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2019.05.021
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: Self-regulated learning (SRL) is crucial for academic success; therefore support, to enhance and maintain SRL skills is important. In blended adult education, the heterogeneity of adults creates diversity in SRL abilities, which makes it necessary to provide tailored support. Conducting latent profile analyses for a sample of 213 blended adult students, we identified three profiles, namely high, low, and moderate SRL profiles which prove differences in SRL strategy use and imply tailored SRL support. Through multivariate analysis of variance (MANOVA) and multinomial logistic regression, we further explore the differences in SRL between the profiles and the extent to which the students' personal background characteristics and achievement motivations predict their profile membership. The three profiles differ significantly in terms of the scores of all SRL subscales. Furthermore, only achievement motivation – more specifically, attainment and utility value – predicts profile membership. These results inform educational practice about opportunities for supporting and enhancing SRL skills. Anticipating attainment and utility value, time management, and collaboration with peers are all recommended. More specifically, teachers can, for example, use authentic tasks and examples during the learning process or be a role model regarding online interaction and information sharing. Highlights: There are three online self-regulation profiles among blended adult students. Attainment and utility value predict online self-regulation profile membership. Authentic tasks tailored to the students' situations raise the task usefulness. Teachers should urge and support students to share their time plans. Teachers should act as role models and promote sharing efficient SRL strategies.