Bridging the Gap in Online Learning Anxiety Among Different Generations in Health Professions Education

Elshami, Wiam; Saravanan, Coumaravelou; Taha, Mohamed; Abdalla, Mohamed Elhassan; Abuzaid, Mohamed M.; Kawas, Sausan Al

doi:10.18295/squmj.4.2021.040

Cited by 11 publications

(7 citation statements)

References 38 publications

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“…Some studies suggest users with high anxiety might show increased online interactions (Dutta et al, 2018), which may not hold true or even be misleading in the online learning context. Our results further provide opportunities to improve the understanding of the role of anxiety in the online learning process, which also offers insights to integrate the disconnected and conflicting views in prior studies (Elshami et al, 2021; Gangaiamaran & Pasupathi, 2017; Warner et al, 2020).…”

Section: Discussionsupporting

confidence: 56%

“…Prior studies suggested that people with higher anxiety may feel pressure, which results in the decline of learning behaviour (Peltier et al, 2021). For example, prior studies demonstrated that anxiety negatively predicted the students' language achievement (Zheng & Cheng, 2018) and the e‐learning process (Elshami et al, 2021; Warner et al, 2020). Our results further confirmed that a significant difference in learning outcomes occurs even if these learners maintain the same volume of online learning activities (No significant direct effect of anxiety on learning habit was found in our study).…”

Section: Discussionmentioning

confidence: 99%

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Anxiety only makes it worse: Exploring the impact mechanisms of app‐based learning on performance progress

Chang

Zhao

et al. 2022

Computer Assisted Learning

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Background Mobile devices facilitate learning activities in a self‐paced way. However, the current understanding of learning participation and its consequence are minimal when learners take advantage of opportunities provided by mobile technologies worldwide. Aims The primary purpose of this study is to examine the effectiveness of environmental incentives on app learning behaviour and learning performance based on a multi‐dimensional approach. We also evaluate whether the role of anxiety attenuates these influences. Method and objective In this study, 323 participants’ learning activities were tracked weekly to measure the completion of their daily learning plan during 16 weeks. Then, an online survey was conducted to assess the environmental incentives, perceptions, and self‐evaluations of these participants after the learning tracking process. Participants’ learning progress was measured by the comparison of paired course test scores before and after the tracking period. Results and Conclusions The results indicated that engagement, sense of meaning, and stage accomplishment exerted a directly significant influence on shaping the learning habits and outcomes. In addition, the results also tapped the role of anxiety in the app learning context, indicating the significant moderating effects on the relationship between social incentives, stage accomplishment, and learning habits. The app learning habits of the users with high‐level anxiety exerted less impact on their performance progress than users with low‐level anxiety. This study provides insights into how the mobile environment can influence learning activities and learning performance, implying practical implications to leverage mobile technologies for educational purposes.

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Section: Discussionsupporting

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Anxiety only makes it worse: Exploring the impact mechanisms of app‐based learning on performance progress

Chang

Zhao

et al. 2022

Computer Assisted Learning

View full text Add to dashboard Cite

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“…The items illustrate the features of relaxed feelings in learning, testing, and using technology online, and being confident in language competence. Likewise, previous studies indicate that students who had prior online experiences experienced less anxiety (Elshami et al, 2021;Pichette, 2009). The findings showed that while some students had anxiety in online learning, their counterparts had positive feelings in online learning and enjoyed it.…”

Section: Discussionsupporting

confidence: 53%

“…Likewise, the Thai university students had a significant level of the online learning anxiety (Chimwong, 2020). The samples in this study are aged between 19 and 22, categorized as a Generation Z (born after 1997) group, who mostly experienced anxiety during online learning, specifically when they had online discussions and written communication (Elshami et al, 2021). In the current study, the EFL students' possible levels of online learning anxiety were evaluated by the pre-screening online questionnaire.…”

Section: Online Learning Anxietymentioning

confidence: 99%

Online Language Anxiety in Higher Education: Evidence from Exploratory and Confirmatory Factor Analyses

Hongnaphadol

2023

rEFLections

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The objective of this research is to identify the underlying components of English language learners’ anxiety in online learning during the COVID-19 and to verify the coherence of the component model with the empirical data. A total of 408 Thai EFL university students, who were selected via a simple random sampling method, were assessed with the Online World Languages Anxiety Scale (OWLAS). An exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) were conducted using LISREL 8.80 to verify the factor structure of Online English Language Anxiety (OELA). The results of the EFA indicate six emerged underlying components: 1) use of English skills online, 2) negative feelings in online learning, 3) positive feelings in online learning, 4) online interaction, 5) cross-linguistic interference, and 6) attitudes to self and English classes. Considering the indices, the model was fit with the empirical data, with X2 = 1115.24, df = 625, p-value = 0.048, X2/df = 1.784, GFI = 0.96, AGFI = 0.97, CFI = 0.99, SRMR = 0.043, RMSEA = 0.045. Factor loading values of each index were between 0.50-0.88. The indicators that had the highest weight value or most significantly influenced students’ OELA were ‘attitudes to self and English classes’ and the least was ‘cross-linguistic interference’.

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“…Changes in social media, along with developments in how people access the internet using gadgets or smartphones that can easily be accessed anytime and anywhere, means that every day people encounter promotions on social media carried out by marketers [42][43][44]. This emerging potential will be even more tremendous when targeting and reaching the generation of Millennials and Gen Xennials through online channels or via the internet [45,46].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Social Media Influencers Raffi Ahmad and Nagita Slavina on Tourism Visit Intentions across Millennials and Zoomers Using a Hierarchical Likelihood Structural Equation Model

et al. 2022

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Background: In this paper, we examine how social media influencers can influence visit intention, especially in the case of Raffi Ahmad and Nagita Slavina, a top influencer who by 2 September 2021 had reached 21.3 M subscribers on YouTube and 54.9 m followers on Instagram with an engagement rate of 0.42%. The focus of this study is Generation Y or Millennials (born 1981–1996) and Generation Z (born 1997–2012). Design/methodology/approach: Snowball sampling was performed to arrive at a representative group of Millennials. Data analysis was performed using hierarchical likelihood via structural equation modeling. Findings: The study results are helpful for a comprehensive understanding of factors affecting visit intention. Effects of the study results summary, tourists from Generations Y and Z are thriving within the internet of things and the digital age, an era in which information can be accessed via various forms of technology across multiple platforms. Practical implications: We discuss and identify the relative importance of each factor through the use of logistics with variational approximation and structural equation models using hierarchical likelihood. Originality: The technique we use is an integrated and extended version of the structural equation model with hierarchical likelihood estimation and features selection using logistics variational approximation.

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Bridging the Gap in Online Learning Anxiety Among Different Generations in Health Professions Education

Cited by 11 publications

References 38 publications

Anxiety only makes it worse: Exploring the impact mechanisms of app‐based learning on performance progress

Anxiety only makes it worse: Exploring the impact mechanisms of app‐based learning on performance progress

Online Language Anxiety in Higher Education: Evidence from Exploratory and Confirmatory Factor Analyses

The Impact of Social Media Influencers Raffi Ahmad and Nagita Slavina on Tourism Visit Intentions across Millennials and Zoomers Using a Hierarchical Likelihood Structural Equation Model

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