Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model

Salloum, Said A.; AlHamad, Ahmad; Al-Emran, Mostafa; Monem, Azza Abdel; Shaalan, Khaled

doi:10.1109/access.2019.2939467

Cited by 375 publications

(281 citation statements)

References 78 publications

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“…In many earlier studies, the researchers have been using TAM to represent the theoretical model [21,24,[47][48][49]. This was based on "users' perceived acceptance of any technology".…”

Section: The Technology Acceptance Modelmentioning

confidence: 99%

“…With the adoption of TAM, the critical factors for the acceptance of E-learning had been identified by several researchers [24,[54][55][56][57][58][59][60][61][62]. Regarding this study, the goal was to analyze the E-learning acceptance behavior of the UAE University students, through whom, better understandings of implications to implement E-learning systems in UAE universities successfully could be gained.…”

Section: Determinants Of Students' Acceptance Of E-learning"mentioning

confidence: 99%

“…The social influence refers to "The degree to which an individual perceives that most people who are important to him think he should or should not use the system" [78,79]. In some situations, a system may be used by people to meet the needs of other people, rather than to concentrate on their own beliefs and feelings [17,24,80]. Several studies have been carried out regarding the impact of social influence on the learners' e-learning adoption or acceptance [57,[81][82][83][84][85][86][87].…”

Section: Social Influence (Si)"mentioning

confidence: 99%

“…There are other issues inherent in TAM as well, for example, the irregularities in the previous studies, and its inefficiency in justifying the social influence on the acceptance of technology [8,22,23]. These limitations will be addressed in the study by extending TAM so that it incorporates other factors like social influence" [17], "perceived enjoyment" [24], "self-efficacy". The key objectives of this study are to cater to the limitations discussed earlier.…”

Section: Introductionmentioning

confidence: 99%

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Investigating a theoretical framework for e-learning technology acceptance

Kurdi

Alshurideh

Salloum

2020

IJECE

Self Cite

114

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E-learning has gained recognition and fame in delivering and distributing educational resources, and the same has become possible with the occurrence of Internet and Web technologies. The research seeks to determine the factors that influence students' acceptance of E-learning and to find out the way these factors determine the students' intention to employ E-learning. A theoretical framework was developed based on the technology acceptance model (TAM). To obtain information from the 270 university students who utilized the E-learning system, a questionnaire was formulated. The results revealed that “social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use” are the strongest and most important predictors in the intention of and students towards E-learning systems. The outcomes offer practical implications for practitioners, lawmakers, and developers in effective E-learning systems implementation to improve ongoing interests and activities of university students in a virtual E-learning atmosphere, valuable recommendations for E-learning practices are given by the research findings, and these may turn out to be as guidelines for the efficient design of E-learning systems.

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“…In many earlier studies, the researchers have been using TAM to represent the theoretical model [21,24,[47][48][49]. This was based on "users' perceived acceptance of any technology".…”

Section: The Technology Acceptance Modelmentioning

confidence: 99%

Section: Determinants Of Students' Acceptance Of E-learning"mentioning

confidence: 99%

Section: Social Influence (Si)"mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigating a theoretical framework for e-learning technology acceptance

Kurdi

Alshurideh

Salloum

2020

IJECE

Self Cite

114

View full text Add to dashboard Cite

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“…According to Table 1, the factors are usually used to assess e-learning, and blended learning systems such as: student or their activity on systems, shown in [7][8][9]14]. The teacher information and affectivity of them are shown in [8,9].…”

Section: Affected Factorsmentioning

confidence: 99%

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

2020

Education Sciences

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The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%.

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