A neural network analytical model for predicting determinants of mobile learning acceptance

Aloqaily, Ahmad; Nawayseh, Mohammad K. Al; Baarah, Aladdin; Salah, Zaher; Hassan, Malak Al; Ghuwairi, Abdel Rahman Al

doi:10.1504/ijcat.2019.099502

Cited by 12 publications

(10 citation statements)

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“…Thus, the findings reveal that diversity in assessment, teacher attitude and response, and the quality of technology have a significant impact on student satisfaction. The performance evaluation of FLN, artificial neural network (ANN) [28], k-nearest neighbors (KNN), and multilayer perceptron (MLP) [25] algorithms on a dataset based on sensitivity, accuracy, and specificity, as shown in Figure 3. The results showed that the FLN model succeeded in predicting student satisfaction.…”

Section: Resultsmentioning

confidence: 99%

“…In this work, opinion words are categorized based on their frequency of appearance in all the texts under study. In Aloqaily et al [28] presented a work that used multi-analytics: neural network (NN) and multiple linear regression models for experimental exploration and predicts factors that influence students' behavioral intention for accepting M-learning. Guo et al [29] used an unsupervised sparse auto-encoder approach to create a classification model from learners' unlabeled data.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of student satisfaction on mobile-learning by using fast learning network

Sultan

Abdulateef

Shtyat

2022

IJEECS

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The rapid advancement of mobile technologies over the past decade has had a significant impact on the appearance of M-learning applications. The research proposes the fast learning network model to investigate and identify the factors that affect student satisfaction in M-learning for the University of Tikrit students. The research model is conducted utilizing a questionnaire of 300 participating students based on variables. This research showed that the proposed model's perfor mance was superior to artificial neural network, <br /> k-nearest neighbors, and multilayer perceptron algorithms. The accuracy and specificity of predicting the student satisfaction coefficients in M-learning were 91.6% and 92.85%, respectively. The proposed findings demonstrate that diversity in the evaluation, teacher attitude and response, and quality of technology are key operators of student satisfaction.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of student satisfaction on mobile-learning by using fast learning network

Sultan

Abdulateef

Shtyat

2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…In Aloqaily et al. (2019), ANN is used to explore and predict the reasons why students accept to use mobile learning in Jordan. Another application can be found in Zhang et al.…”

Section: Analytical Methodologies In Developing Countriesmentioning

confidence: 99%

“…Aloqaily et al. (2019) explore and predict the reasons why students find mobile learning acceptable in Jordan. Al‐Shihi et al.…”

Section: Application Areasmentioning

confidence: 99%

Analytics in developing countries: methods, applications, and the impact on the UN Sustainable Development Goals

Orhan

Guajardo

2021

Int Trans Operational Res

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The growing availability of data coupled with advanced methods and computing power have fostered organizations to adopt business analytics in their decision‐making. Nevertheless, the application of analytics is subject to available resources and capabilities, and, therefore, differs from region to region. Developing countries in particular are exposed to circumstances that can limit the expansion of analytics within their domains. This may prohibit the acceleration of development efforts and, consequently, may result in delays in achieving the United Nations Sustainable Development Goals (UNSDGs) set for 2030. Taking this concern as a starting point, this paper provides the results of a survey on the status of analytics research in developing countries. Through a thorough review and classification of 142 articles published during 2009–2019, we characterize analytics in developing countries along three dimensions. First, we explore the mainstream methodologies appearing in the literature, which range from classic optimization techniques to more recent statistical and machine learning methods. Second, we identify 11 application areas that cover a broad range. Third, we focus on the linkages between the reviewed papers and the 17 UNSDGs. Along this dimension, we establish a connection between the literature and 13 goals, and illustrate how some of these contributions relate to some of the 169 targets set by United Nations. Goals such as gender equality are addressed to either little or no extent by the surveyed literature. Also, the survey reveals no adoption of indicators that are set to track the progress toward the targets.

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“…There are plenty studies in marketing literature about behavioural intention and use in using m-learning [26]; [12]; [5]; [25]; [2]; [6]; [1]; [42]; [30]; [16]; [11]; [8]; [44]; [13]; [37]; [43]; [27]; [38]; [9]; [23]; [22]; [47]; [4]; [14]; [3]; [48]; [10]; [51]; [34]; [31]; [53]. In these studies, the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Unified Theory of Acceptance and Use of Technology (UTAUT2) are generally used as basic models.…”

Section: Introductionmentioning

confidence: 99%

The Antecedents of the Consumers' Mobile Learning Intention during the Covid-19 Pandemic

Armağan

Durukal²

2021

ijssei

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Abstract Purpose Covid-19, which affects the whole world, has caused serious changes in many aspects such as lifestyle, habits and purchasing behaviour. New digital consumers and companies that emerged during the epidemic; they realized that mobile devices, especially mobile phones, have become a solution to many real-world problems such as learning and education anytime and anywhere. This study aims to determine the factors affecting the users' mobile learning (m-learning) usage intention during the Covid-19 pandemic process. Design/methodology/approach – This study contributed to the confirmation of the extended TAM model for a mobile device. The sample of the questionnaire is 460 students from different universities in Turkey. These data obtained were analyzed with the Structural Equation Model (SEM) and LISREL program was used for data analysis. Findings – This study proffers a model that the antecedents of the users' mobile learning intention during the Covid-19 pandemic. According to the results of the study, it was concluded that the factor affecting the users' mobile learning (m-learning) intention to use the most is perceived ease of use, the least effective factor is intrinsic leisure motivation, and the future anxiety factor has a meaningless effect. Originality/value – A holistic view of the antecedents of the users' mobile learning intention during the Covid-19 pandemic would be of important use to practitioners and academics alike. This study is different from previous studies; It is thought that it will contribute to the literature by addressing the effects of internal leisure motivation, future anxiety, behavioural spread and system-service quality dimensions on m-learning. For the researchers, this study took an important step towards explaining the m-learning relationship with students' intrinsic leisure motivation, future anxiety, behavioural spread, and system-service quality learning perspectives. Keywords Mobile learning, Mobile CRM, Technology Acceptance Model

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A neural network analytical model for predicting determinants of mobile learning acceptance

Cited by 12 publications

References 0 publications

Prediction of student satisfaction on mobile-learning by using fast learning network

Prediction of student satisfaction on mobile-learning by using fast learning network

Analytics in developing countries: methods, applications, and the impact on the UN Sustainable Development Goals

The Antecedents of the Consumers' Mobile Learning Intention during the Covid-19 Pandemic

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