An intervention study for the development of self-regulated learning skills

Cazan, Ana-Maria

doi:10.1007/s12144-020-01136-x

Cited by 17 publications

(17 citation statements)

References 44 publications

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“…Student-centered learning in online learning environments should be supported with the use of pedagogical tools, such as multimedia tools and asynchronous and synchronous communication channels, to facilitate educational interactions with instructors and peers. Simultaneously, instructors are pivotal in enhancing students' SRL and so they must deliver the curricula and seek innovative methods to regularize and embed the skills of SRL in the learning activities [103,104]. Training courses might be useful in developing SRL strategies (i.e., time management, planning, goal setting), especially at early stages in study programs [105].…”

Section: Practical Implicationsmentioning

confidence: 99%

Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach

et al. 2021

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In higher education learning, e-learning systems have become renowned tools worldwide. The evident importance of e-learning in higher education has resulted in a prenominal increase in the number of e-learning systems delivering various forms of services, especially when traditional education (face-to-face) was suddenly forced to move online due to the COVID-19 outbreak. Accordingly, assessing e-learning systems is pivotal in the interest of effective use and successful implementation. By relying on the related literature review, an extensive model is developed by integrating the information system success model (ISSM) and the technology acceptance model (TAM) to illustrate key factors that influence the success of e-learning systems. Based on the proposed model, theory-based hypotheses are tested through structural equation modeling employing empirical data gathered through a survey questionnaire of 537 students from three private universities in Jordan. The findings demonstrate that quality factors, including instructor, technical system, support service, educational systems, and course content quality, have a direct positive influence on students’ satisfaction, perceived usefulness, and system use. Moreover, self-regulated learning negatively affects students’ satisfaction, perceived usefulness, and system use. Students’ satisfaction, perceived usefulness, and system use are key predictors of their academic performance. These findings provide e-learning stakeholders with important implications that guarantee the effective, successful use of e-learning that positively affects students’ learning.

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Section: Practical Implicationsmentioning

confidence: 99%

Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach

et al. 2021

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“…Associations between the scholars SRL and tutorial action were known, and several other implications and proposals were derived. Cazan (2020) [8] investigated the development of self-regulated learning skills. The study relies on the social-cognitive perspective of self-regulated learning and focuses on its development in educational settings.…”

Section: Self-regulated Learningmentioning

confidence: 99%

Learning styles: A detailed literature review

Yadav¹,

Shukla²

2021

Int. J. Appl. Res.

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Fundamental elements of learning can be an inevitable witness. A lot of pedagogic studies have been carried out to explore and analyse various learning styles. Many authors have projected that the flexibility to typify student learning styles will augment the academic experiences. The current study offers attention to the different aspects of learning and discusses the constraints and possibilities of learning styles in academic studies.

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“…A combination of various learning techniques embedded into a course structure has also proven to be effective in promoting self-regulation in students. Cazan (2020) developed an intervention that is comprised of learning journals, concept maps, error analysis tasks, self-and peer-assessment tasks. In that study, students were trained to use techniques through implicit training as these techniques were part of the coursework.…”

Section: Implications For Higher Educationmentioning

confidence: 99%

A Comparative Study of Self-Regulation Levels and Academic Performance among STEM and Non-STEM University Students Using Multivariate Analysis of Variance

Bene¹,

Lapina²,

Birida³

et al. 2021

tused

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Based on the assumption that STEM students may exhibit higher self-regulation levels than non-STEM students, this study compared the levels of self-regulation variable across four fields of study. By employing multivariate analysis, it was found that differences in self-regulation levels among STEM and non-STEM students predict students’ GPA. STEM students reported higher self-regulation levels than non-STEM students. However, among the STEM fields, only engineering students displayed statistically significant difference in self-regulation levels when compared to non-STEM fields of study. Computer science students displayed a significantly higher GPA than law and business administration students, with engineering students displaying the second highest and statistically non-significant GPA. A regression analysis revealed that students’ self-regulation levels significantly predicted students’ GPA. The findings add value to the psychological concept as an important element in the context of learning in higher education. It was concluded that self-regulation remains an essential skill that enhances students’ effectiveness and need to be emphasized in the orientation for life in College.

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An intervention study for the development of self-regulated learning skills

Cited by 17 publications

References 44 publications

Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach

Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach

Learning styles: A detailed literature review

A Comparative Study of Self-Regulation Levels and Academic Performance among STEM and Non-STEM University Students Using Multivariate Analysis of Variance

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