University Students’ Perception of Video-Based Learning in Times of COVID-19: A TAM/TTF Perspective

Pal, Debajyoti; Patra, Syamal

doi:10.1080/10447318.2020.1848164

Cited by 140 publications

(118 citation statements)

References 95 publications

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“…Furthermore, the model’s discriminant validity is assessed by two means, first by the AVE square root of each variable, and secondly by the HTMT ratio of correlations. Pal and Patra [ 41 ] also used both means, based on the criticism about the use of the Fornell and Larcker [ 55 ] criterion for measuring the discriminant validity as not always an effective mean for evaluating this type of validity. Our measurements of the AVE square root values of the inter-item correlation matrix supported the conditions of discriminant validity.…”

Section: Resultsmentioning

confidence: 99%

“…The participants were asked to evaluate all five constructs, namely, PI, PP, PU, PEOU, and BI, by rating their training experience using a 7-point Likert scale, ranging from (1) strongly disagree to (7) strongly agree. The variables of the proposed model are assessed using indicators ( Table 2 ), adopted from validated questionnaires [ 40 , 41 , 42 , 43 ] from previous related studies, and adapted according to the purpose of our study.…”

Section: Evaluation Proceduresmentioning

confidence: 99%

“…PLS is a strong candidate for data analysis because of the exploratory nature of our sample, where the primary goal is to understand the variance of the dependent variables induced by the independent variables, rather than the goodness of fit between the model and data [ 41 ].…”

Section: Evaluation Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

Measuring User Experience, Usability and Interactivity of a Personalized Mobile Augmented Reality Training System

Papakostas

Troussas

Krouska

et al. 2021

Sensors

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Innovative technology has been an important part of firefighting, as it advances firefighters’ safety and effectiveness. Prior research has examined the implementation of training systems using augmented reality (AR) in other domains, such as welding, aviation, army, and mathematics, offering significant pedagogical affordances. Nevertheless, firefighting training systems using AR are still an under-researched area. The increasing penetration of AR for training is the driving force behind this study, and the scope is to analyze the main aspects affecting the acceptance of AR by firefighters. The current research uses a technology acceptance model, extended by the external constructs of perceived interactivity and personalization, to consider both the system and individual level. The proposed model was evaluated by a sample of 200 users, and the results show that both the external variables of perceived interactivity and perceived personalization are prerequisite factors in extending the TAM model. The findings reveal that the usability is the strongest predictor of firefighters’ behavioral intentions to use the AR system, followed by the ease of use with smaller, yet meaningful, direct and indirect effects on firefighters’ intentions. The identified acceptance factors help AR developers enhance the firefighters’ experience in training operations.

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Section: Resultsmentioning

confidence: 99%

Section: Evaluation Proceduresmentioning

confidence: 99%

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Measuring User Experience, Usability and Interactivity of a Personalized Mobile Augmented Reality Training System

Papakostas

Troussas

Krouska

et al. 2021

Sensors

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show abstract

“…One thing has to be mentioned that before the emergence of COVID-19, the usage of MOOC platforms and their resources was mostly supplementary in addition to the regular classroom instruction delivered in schools and universities [19]- [21]. As a result of the COVID-19 scenario, it impacted all types of learning institutions worldwide, ranging from schools to colleges and universities, and an unprecedented situation has arisen, with a drastic shift in the mode of education delivery to completely online-only [20], [36]. Therefore, lecturers have been forced to use some type of online delivery platform to deliver their courses.…”

Section: A the Continuance Usage Of Moocmentioning

confidence: 99%

“…Therefore, teaching and learning are entirely online. This sudden outbreak of the pandemic left educational institutions with little time to plan and equip themselves with technology, solutions, and policies that would allow them to go completely virtual without affecting students' learning [20], [23]. For this reason, we must devise solutions and try to find out factors that lead to the drawbacks of MOOCs and give further clarifications on how we can improve them.…”

Section: Introductionmentioning

confidence: 99%

How Gamification Leads to Continued Usage of MOOCs? A Theoretical Perspective

et al. 2021

Self Cite

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The high dropout rate, lack of learners' motivation, and MOOC users' diversity are significant issues in MOOCs. In this sense, gamification is used to investigate if it can help to increase MOOC learners' motivation and engagement, leading to a continued usage scenario. Further, this article aims to propose a theoretical model to identify the factors affecting MOOC learners' continuance and empirically measure these factors. The model is based on the Expectation Confirmation Model (ECM) with additional constructs of motivation and gamification. Data is collected from 206 university students using an online survey. Structural Equation Modeling (SEM) is used for data analysis. The results show that motivation, satisfaction, and perceived usefulness influence continuous intention, with satisfaction being the most significant predictor (β = 0.373, p < 0.000). Motivation, confirmation, and perceived usefulness have a significant positive effect on satisfaction. Among the three gamification categories, achievement has the highest impact on motivation (β = 0.208, p < 0.001), followed by the social category (β = 0.1 43, p < 0.032). The effect of the immersion category is found to be non-significant. Based on the results, appropriate theoretical and practical implications are discussed.

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Evolution of TAM

Davis,

Granić

2024

Human–Computer Interaction Series

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University Students’ Perception of Video-Based Learning in Times of COVID-19: A TAM/TTF Perspective

Cited by 140 publications

References 95 publications

Measuring User Experience, Usability and Interactivity of a Personalized Mobile Augmented Reality Training System

Measuring User Experience, Usability and Interactivity of a Personalized Mobile Augmented Reality Training System

How Gamification Leads to Continued Usage of MOOCs? A Theoretical Perspective

Evolution of TAM

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