Self–efficacy and student satisfaction in the context of blended learning courses

Prifti, Rezart

doi:10.1080/02680513.2020.1755642

Cited by 111 publications

(80 citation statements)

References 54 publications

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“…Also, self-efficacy, defined as the individuals' belief in their own ability to perform a certain task, challenge or successfully engage with educational technology [38,39], showed to be interconnected with student satisfaction levels [40]. Recently, Prifti (2020) identified that the learning management system self-efficacy positively influenced student satisfaction in blended learning in Tirana of Albania while both platform content and accessibility were important constructs affecting the self-efficacy level [41]. Similarly, Geng et al (2019) found technology readiness positively influenced learning motivation during blended learning in higher education [42].…”

Section: Readiness and Accessibilitymentioning

confidence: 99%

Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques

2021

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Despite the wide adoption of emergency remote learning (ERL) in higher education during the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting student satisfaction for this novel learning environment in crisis. The present study investigated important predictors in determining the satisfaction of undergraduate students (N = 425) from multiple departments in using ERL at a self-funded university in Hong Kong while Moodle and Microsoft Team are the key learning tools. By comparing the predictive accuracy between multiple regression and machine learning models before and after the use of random forest recursive feature elimination, all multiple regression, and machine learning models showed improved accuracy while the most accurate model was the elastic net regression with 65.2% explained variance. The results show only neutral (4.11 on a 7-point Likert scale) regarding the overall satisfaction score on ERL. Even majority of students are competent in technology and have no obvious issue in accessing learning devices or Wi-Fi, face-to-face learning is more preferable compared to ERL and this is found to be the most important predictor. Besides, the level of efforts made by instructors, the agreement on the appropriateness of the adjusted assessment methods, and the perception of online learning being well delivered are shown to be highly important in determining the satisfaction scores. The results suggest that the need of reviewing the quality and quantity of modified assessment accommodated for ERL and structured class delivery with the suitable amount of interactive learning according to the learning culture and program nature.

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Section: Readiness and Accessibilitymentioning

confidence: 99%

Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques

2021

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“…In addition, in online courses, students' ISE was a significant predictor of their satisfaction (Alqurashi, 2018;Kuo et al, 2014). Similarly, students' ISE had a direct impact on course satisfaction (Prifti, 2020;Wei & Chou, 2020). Furthermore, Alqurashi (2020) identified that online self-efficacy was a powerful predictor, as well as a vital contributor, to perceive satisfaction in online learning.…”

Section: Internet Self-efficacy and Online Learning Satisfactionmentioning

confidence: 99%

“…Moreover, researchers have demonstrated a strong connection between the motivation to learn online and participants' success and engagement in online learning settings (Keskin & Yurdugül, 2020). ing (Alqurashi, 2018;Kuo et al, 2014Kuo et al, , 2010Muhsin et al, 2019;Prifti, 2020;Tawfik et al, 2017;Wei & Chou, 2020). Moreover, onlinebased DL can impact students' OLM (Thai et al, 2017).…”

Section: The Mediating Role Of Online Learning Motivationmentioning

confidence: 99%

Investigating the mediating role of online learning motivation in the COVID‐19 pandemic situation in Bangladesh

Rahman

Uddin

Dey

2021

Computer Assisted Learning

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The purpose of this paper is to investigate the mediating role of online learning motivation (OLM) in the COVID‐19 pandemic situation in Bangladesh by observing and comparing direct lectures (DL), instructor–learner interaction (ILI), learner–learner interaction (LLI), and internet self‐efficacy (ISE) as predictors of OLM and online learning satisfaction (OLS). Data were collected from 442 undergraduate and graduate students from more than 35 universities in Bangladesh. To test the hypotheses, the PLS‐SEM approach was applied using SmartPLS 3.0. The study shows a significant mediating role of OLM between the independent variables and learning satisfaction. In addition, DL, ILI, and ISE are shown to be significant predictors of student satisfaction. The findings have a number of valuable implications for education policy makers, universities, instructors, and students. Moreover, the study suggests some new research perspectives to overcome the limitations of this research and to gain precise knowledge on students' learning motivation and satisfaction regarding other online classes for different categories of students (e.g., high school and college, professional, and PhD).

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“…Relative research has defined the concept of student's IT skills and self-learning under the term self-efficacy, which refers broadly to the efforts and capabilities of a student toward executing and organizing successfully required course tasks [12], [33], [34], [35]. In this research, student's IT skills (SITS) factor refers to the student's technical ability to use the LMS software effectively.…”

Section: Students' Perceived Benefitsmentioning

confidence: 99%

Faculty e-Learning Adoption During the COVID-19 Pandemic: A Case Study of Shaqra University

Alshehri¹,

Alahmari²

2021

IJACSA

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Self–efficacy and student satisfaction in the context of blended learning courses

Cited by 111 publications

References 54 publications

Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques

Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques

Investigating the mediating role of online learning motivation in the COVID‐19 pandemic situation in Bangladesh

Faculty e-Learning Adoption During the COVID-19 Pandemic: A Case Study of Shaqra University

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