Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors

Investigating the influence of the most commonly used external variables of TAM on students' Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios

Azerbaijan has successfully incorporated modern Information Communication Technologies (ICT) in the education system. The major goal is to raise the standard of education. The factors that affect university students' behavioral intention (BI) to use e-learning for educational purposes in Azerbaijan are worthy of study. This is an empirical study of the use of the General Extended Technology Acceptance Model for E-learning (GETAMEL) developed by Abdullah and Ward (2016) in order to determine the factors that affect undergraduate students' BI to use an e-learning system. The data was collected from 714 undergraduate and masters students using a convenient sampling technique and the responses were analyzed using Structural Equation Modeling (SEM). It is seen that the Subjective norm (SN), Experience (EXP) and Enjoyment (ENJOY) positively and significantly influence students' perceived usefulness (PU) of e-learning, while Computer anxiety (CA) has a negatively effect. EXP, ENJOY and Self-efficacy (SE) positively and significantly affect their perceived ease of use (PEOU) of e-learning. It is also seen that SN has a positive and significant impact on BI to use e-learning, while Technological innovation (TI) significantly moderates the relationship between SN and PU, PU and BI to use e-learning. This study is the first to determine a negative and significant relationship between CA and PU, in the context of students e-learning. This study is also one of the very few that uses the GETAMEL model for e-learning settings. The results have significant practical implications for educational institutions and decision-makers, in terms of the design of the e-learning system in universities. Display Omitted The GETAMEL model successfully applies to e-learning acceptance domain.Computer anxiety negatively affects perceived usefulness and perceived ease of use.Subjective norm does not have an impact on perceived ease of use of e-learning system.Self-efficacy does not affect perceived usefulness of e-learning system.perceived ease of use does not affect perceived usefulness of e-learning system.

Examining the students behavioral intention to use e-learning in Azerbaijan? The General Extended Technology Acceptance Model for E-learning approach

Today’s rapid changing world highlights the influence and impact of technology in all aspects of learning life. Higher Education institutions in developed Western countries believe that these developments offer rich opportunities to embed technological innovations within the learning environment. This places developing countries, striving to be equally competitive in international markets, under tremendous pressure to similarly embed appropriate blends of technologies within their learning and curriculum approaches, and consequently enhance and innovate their learning experiences. Although many universities across the world have incorporated internet-based learning systems, the success of their implementation requires an extensive understanding of the end user acceptance process. Learning using technology has become a popular approach within higher education institutions due to the continuous growth of Internet innovations and technologies. Therefore, this paper focuses on the investigation of students, who attempt to successfully adopt e-learning systems at universities in Jordan. The conceptual research framework of e-learning adoption, which is used in the analysis, is based on the technology acceptance model. The study also provides an indicator of students’ acceptance of e-learning as well as identifying the important factors that would contribute to its successful use. The outcomes will enrich the understanding of students’ acceptance of e-learning and will assist in its continuing implementation at Jordanian Universities.

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Exploring Students Acceptance of E-Learning Using Technology Acceptance Model in Jordanian Universities.

Perceived Usability Evaluation of Microsoft Teams as an Online Learning Platform During COVID-19 using System Usability Scale and Technology Acceptance Model in India.

Usability factors predicting continuance of intention to use cloud e-learning application.

Face spoofing attack is one of the recent security traits that face recognition systems are proven to be vulnerable to. The spoofing occurs when an attacker bypass the authentication scheme by presenting a copy of the face image for a valid user. Therefore, it’s very easy to perform face recognition spoofing attack with compare to other biometrics. This paper, addresses the problem of detecting imposter face image from live image. In practically, we address this problem from texture analysis point of view because the printed face usually has less quality defect that can be observed by extracting texture features. We adopt Local graph structure LGS to extract the features. Moreover, LGS is based on applying a dominant graph into the input image and it’s proved to be a powerful texture operator. Finally, extensive experimental analysis on NUAA showed an encouraging performance.

Face Spoofing Detection Based on Improved Local Graph Structure

The purpose of this study was to develop Learning Object Acceptance Model (LOAM) to examine the underlying factors and causal relationships that determine learners' behavioral intention to use multimedia learning objects in higher education. This study called for the 342 respondents to progress through two phases of learning objects participation: Introduction and Direct-use experience. Structural Equation Modeling (SEM) was used to evaluate data from the resultant surveys with the AMOS 4.0. The results showed that the study model produced measurement and structural models with adequate model fits. Learners' perceived of usefulness and ease of use a fully mediated the relationship between learning object characteristics and behavioral intention. However, individual characteristics were found to have no statistically significance on behavioral intention in this context.

An Empirical Study of Learning Object Acceptance in Multimedia Learning Environment

Technological progress has resulted in constant change of social structures and led to continuous evolution of attitudes and values. Thus, a timely and compre-hensive integrated technology acceptance framework was established, and factors predicting intention to use Cloud e-learning were empirically identified. A total of 11 determinants from varying sources of IS success, learning object criteria, technology acceptance, motivations, social cognitive, and expectancy values were unified and analysed in an integrated manner. The target population for this study was a group of IT students in a private university in southern region Malaysia. The results obtained from this study observed that content quality, pedagogical quality, perceived usefulness, social influence, attainment value, and utility value significantly predict intention to use Cloud e-learning. By integrating IS success, learning object criteria, technology acceptance, motivations, social cognitive, and expectancy values into a single framework, new insights on Cloud e-learning ac-ceptance in higher education can be attained.

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An Empirical Study of Students' Intention to Use Cloud E-Learning in Higher Education

Based on the ongoing development in the Long Term Evolution (LTE), it was brought in to satisfy user's needs and demands on the enhancement of the wireless network's Quality of Service, especially for real-time applications. Real-time applications require low delay; in order to have a better user's experience. Hence, a scheduling framework focuses on real-time applications has been proposed and developed in this study. This paper compares the performance of real-time scheduling frameworks; 1) Modified-Largest Weighted Delay First (M-LWDF), 2) Frame Level Scheduler (FLS) and Proportional Fair downlink scheduler, and 3) our proposed two-level scheduling framework (FLS-M-EXP). Tests are carried out on these scheduling methods, and the data produced are gathered and analyzed. Simulation results showed that the proposed two-level scheduling framework with FLS and Exponential Rule (EXP) has a better performance in terms of delay as compared to the existing scheduling framework, which consists of FLS and Proportional Fair method. The proposed two-level scheduling framework also performs better than the common real-time scheduler, M-LWDF.

Siong Hoe Lau

Papers

Usability factors predicting continuance of intention to use cloud e-learning application.

Face Spoofing Detection Based on Improved Local Graph Structure

An Empirical Study of Learning Object Acceptance in Multimedia Learning Environment

An Empirical Study of Students' Intention to Use Cloud E-Learning in Higher Education

Two-Level Scheduling Framework with Frame Level Scheduling and Exponential Rule in Wireless Network