Predicting Factors Affecting Intention to Use Web 2.0 in Learning: Evidence From Science Education

Muhaimin, Muhaimin; Habibi, Akhmad; Mukminin, Amirul; Pratama, Robin; Asrial, Asrial; Harja, Harlina

doi:10.33225/jbse/19.18.595

Cited by 45 publications

(76 citation statements)

References 30 publications

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“…PEU was significantly predicted by SC. Similarly, it was also informed that supporting or facilitating condition influence PEU [17], [27]. In this study context, supporting infrastructures, training, and technical support can improve PSETs perceived ease of use.…”

Section: Figure 2 Bootstrapping Resultsmentioning

confidence: 97%

“…SN, which is one of the theories of planned behavior variables developed by [18], is defined as PSETs beliefs that their important people would support the use of m-LMS. Findings from previous studies informed that SN has a significant relationship with PU of Web 2.0 use [27] and behavioral intention to use m-learning [40]. H7: SN is significantly predicting PU H8: SN is significantly predicting BI.…”

Section: H Subjective Norms (Sn)mentioning

confidence: 85%

“…SC can be infrastructure availability, technical support, and professional development opportunities, as well as policies to promote the use of m-LMS. In research focusing on students' use of technology, a positive correlation between facilitating condition or supporting condition and PEU was reported [27]. Similarly, in predicting PU, SC was also reported to be significant [37].…”

Section: E Supporting Condition (Sc)mentioning

confidence: 94%

“…TAM has obtained an important eminence because of its transferability to different settings and samples. It has also explained various uses of digital technology in education [24], [25]. The simplicity for structural equation modeling is also one of the TAM benefits [26].…”

Section: Technology Acceptance Model (Tam)mentioning

confidence: 99%

“…The simplicity for structural equation modeling is also one of the TAM benefits [26]. Several studies have utilized TAM to explore technology integration in education [24], [27]- [31].…”

Section: Technology Acceptance Model (Tam)mentioning

confidence: 99%

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Exploring the Drivers Predicting Behavioral Intention to Use m-Learning Management System: Partial Least Square Structural Equation Model

et al. 2020

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This study aimed at reporting the drivers predicting Pre-Service English Teachers' (PSETs) behavioral intention to use m-learning Management Systems (m-LMS) in their learning activities. To achieve the purpose of the study, an extended technology acceptance model (TAM) was administered. Six drivers were included to examine 11 paths of the study framework. The instrument was adapted from previous studies and validated through content validity. Further, it was piloted to 76 PSETs for the reliability. Two hundred and ten responses were analyzed for the main study. The results of the study were obtained by implementing the procedures of Partial Least Squares Structural Equation Modeling (PLS-SEM). The extended TAM-based drivers are reported to be valid and reliable in the measurement model stage. In the assessment of the structural model, nine out of the eleven paths are significant. The strongest significant relationship is reported to emerge between perceive usefulness and attitude toward m-LMS while the weakest significant relationship is reported between self efficacy and perceived usefulness. On the other hand, the two paths that are not positively correlated are between; 1) supporting condition and perceived usefulness; 2) subjective norm and use of m-LMS. Recommendations for future research regarding the behavioral intention to use m-LMS in developing countries are provided.

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Section: Figure 2 Bootstrapping Resultsmentioning

confidence: 97%

Section: H Subjective Norms (Sn)mentioning

confidence: 85%

Section: E Supporting Condition (Sc)mentioning

confidence: 94%

Section: Technology Acceptance Model (Tam)mentioning

confidence: 99%

“…The simplicity for structural equation modeling is also one of the TAM benefits [26]. Several studies have utilized TAM to explore technology integration in education [24], [27]- [31].…”

Section: Technology Acceptance Model (Tam)mentioning

confidence: 99%

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Exploring the Drivers Predicting Behavioral Intention to Use m-Learning Management System: Partial Least Square Structural Equation Model

et al. 2020

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Performance evaluation of accessibility checker tool for educational websites

Seetha

Ayyadurai

2022

Concurrency and Computation

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In recent days, Web 2.0 has become a popular tool including free digital programs which is utilized to create and share student-created projects and products. They are cooperative, multipurpose, easier to utilize digital platforms which boost students for interacting with others or generate and share individualized response products. At the same time, web accessibility checking tools play a vital role in Web 2.0. Therefore, this article aims to investigate the performance of the accessibility checker (AChecker) tool for education and learning websites in Web 2.0. The AChecker validates HTML content for web accessibility issues by providing the location of the web page, uploading an HTML file, or pasting the whole HTML source code from a website. It generates a report of every web accessibility problem for the chosen guidelines for the known, likely, and potential problems. In addition, the AChecker tool is used to examine a set of 25 websites related to education and learning. Brief performance analysis of the AChecker tool takes place using WCAG 1.0 and WCAG 2.0 guidelines. The experimental outcome indicated that the websites are required to be improved for accessibility by everyone.

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Video conferencing in the e-learning context: explaining learning outcome with the technology acceptance model

Bailey

Almusharraf

2022

Educ Inf Technol

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This study uses the technology acceptance model (TAM) to help explain how the use of technology influences learning outcomes emanating from engagement with the Zoom video conference platform. To this end, structural equation modeling was used to analyze the relationships among the TAM variables in reference to Zoom taught during the Covid-19 pandemic. Following a cross-sectional research design, data were collected using Davis's TAM (1989) scales including perceived ease of use (PEoU), perceived usefulness (PU), behavioral intentions, and attitude from 321 South Korean university students attending their 10 th week of English as a foreign language (EFL) conversational English classes. Results revealed that seven of the ten proposed hypotheses were confirmed, with path coefficients having small to large effect sizes. Most notably, PEoU with Zoom strongly affected PU and actual use. In addition, PU with Zoom predicted intentions to use Zoom in the future; however, it failed to influence perceived learning outcomes. While PU predicted future use, it did not influence actual use regarding how well students reported their current performance in their video conference course. PEoU with video conference tools was an influential antecedent to usefulness, attitude, and perceived learning outcome. Lastly, two notable instances of mediation through PU occurred. In consideration of findings, students and instructors should be well trained on the use and functionality of video conference software before its implementation in video conference classrooms.

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Predicting Factors Affecting Intention to Use Web 2.0 in Learning: Evidence From Science Education

Cited by 45 publications

References 30 publications

Exploring the Drivers Predicting Behavioral Intention to Use m-Learning Management System: Partial Least Square Structural Equation Model

Exploring the Drivers Predicting Behavioral Intention to Use m-Learning Management System: Partial Least Square Structural Equation Model

Performance evaluation of accessibility checker tool for educational websites

Video conferencing in the e-learning context: explaining learning outcome with the technology acceptance model

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