scholarly journals Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach

10.2196/24032 ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. e24032
Author(s):  
Iman Akour ◽  
Muhammad Alshurideh ◽  
Barween Al Kurdi ◽  
Amel Al Ali ◽  
Said Salloum
Keyword(s):  
Machine Learning ◽  
Technology Acceptance ◽  
United Arab Emirates ◽  
Mobile Learning ◽  
Teaching And Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Equation Modeling ◽  
Research Model ◽  
Learning Platforms

Background Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide. Objective This study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions. Methods An extended technology acceptance model and theory of planned behavior model were proposed to analyze university students’ adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey. Results Based on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable. Conclusions Our study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic.

scholarly journals Predicting the intention to use mobile learning during Coronavirus Pandemic through Machine Learning Algorithms (Preprint)

2020 ◽  
Author(s):  
Said Abdelrahim Salloum 5th ◽  
Iman Akour Sr ◽  
Muhammad Alshurideh 2nd ◽  
Barween Al Kurdi 3rd ◽  
Amal Al Ali 4th
Keyword(s):  
Machine Learning ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Mobile Learning ◽  
Structural Equation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Equation Modeling ◽  
Learning Platforms ◽  
Acceptance Model

BACKGROUND This paper investigates the use of mobile learning platforms for learning purposes among university students in UAE. An extended Technology Acceptance Model (TAM) and theory of planned behavior (TPB) are proposed to analyze the adoption of mobile learning platforms by university students for accessing course materials, searching the web for information related to their discipline, sharing knowledge, conducting assignments during COVID-19 pandemic. The total number of questionnaires collected was 1880 form different universities. Partial least squares-structural equation modeling (PLS-SEM) and machine learning algorithms (ML) were utilized to investigate the research model based on the student’s data gathered through a survey. According to the results, each hypothesized relationship within the research model has been supported by the data analysis methods. It should also be noted that the J48 classifier mostly had the upper hand on other classifiers when it comes to the prediction of the dependent variable. As per the indication of our research, teaching and learning can greatly benefit from the adoption of machine learning as an educational tool at the time of this pandemic; nevertheless, its significance could be lowered because of the emotion of fear concerning poor grades, stressful family circumstances, and loss of friends. Accordingly, this issue can only be solved by evaluating the emotions of students during this pandemic. OBJECTIVE This study is one of the earliest attempt to: (1) theoretically integrate the notion of fear within a hybrid model of Technology Acceptance Model (TAM) & Theory of Planned Behavior (TPB) (2) empirically test the effect of COVID-19 on the users of mobile application, and (3) explore the impact of the Coronavirus pandemic on users' ability to use the mobile application easily and users' attitude towards the usefulness of mobile learning platform. METHODS The developed theoretical model has been evaluated using two different techniques in this research. The first one involves the usage of the partial least squares-structural equation modeling (PLS-SEM) alongside the SmartPLS tool. This research uses PLS-SEM mainly because both the structural and measurement model can be concurrently analyzed through PLS-SEM, which increases the preciseness of results. As for the second technique, the research predicts the dependent variables entailing the conceptual model with the help of machine learning algorithms via Weka. RESULTS The present research has implemented a model that would be useful for future studies to be conducted since it helps assess the COVID-19 influence at the time of the pandemic period. Keeping the research results in mind, and the fear factor present during the period, the ML is considered to be a significantly useful tool which helps reduce the fear present within the peers and instructors. Similarly, the perceived fear (PF) highly affects the PU and PEU. According to the responses, during the pandemic period, the PF is quite evident; however, the ML maintains a high PU and PEU degree, which reduces the fear factor and encourages the students to participate in their scheduled class. CONCLUSIONS The current research results are similar to the ones presented in earlier research studies related to the TAM and TPB variable’s importance (Ajzen, 1985; F. D Davis, 1989; Teo, 2012; V Venkatesh & Bala, 2008). It is observed that the students are much more acceptable towards technology is there is nothing but the ML technology available as the tool for learning during the COVID-19 pandemic. The PU and PEU related results are also similar to the ones of the earlier PU and PEU related results that influence the student acceptance of ML. Hence, it should be considered as an indicator for the students intention to make use of the ML when the environment is infected with COVID-19. Furthermore, PU is highly affected by PEU, which indicates that if it is easy to use the technology, then it would be considered useful.

Extending the Technology Acceptance Model to Investigate the Utilization of ERP Systems

2010 ◽  
Vol 6 (4) ◽  
pp. 38-54 ◽  
Author(s):  
Samar Mouakket
Keyword(s):  
Technology Acceptance ◽  
United Arab Emirates ◽  
Technology Acceptance Model ◽  
Structural Equation ◽  
Systems Design ◽  
Equation Modeling ◽  
Research Model ◽  
Erp Systems ◽  
Acceptance Model ◽  
The Impact

The implementation of Enterprise Recourse Planning (ERP) systems has grown rapidly, but limited research has been conducted to investigate the utilization of ERP systems. By extending the Technology Acceptance Model, this paper provides a research model for examining the impact of computer self-efficacy and ERP systems design features on the utilization of ERP systems. To test the proposed research model, data are collected through a questionnaire survey distributed among employees in different organizations that have implemented an ERP system in the United Arab Emirates. Structural equation modeling techniques are used in this study to verify the causal relationships between the variables. The results strongly support the extended TAM in understanding employees’ utilization of ERP systems. The implications of this study and further research opportunities are also discussed.

Extending the Technology Acceptance Model to Investigate the Utilization of ERP Systems

2012 ◽  
pp. 1-18 ◽  
Author(s):  
Samar Mouakket
Keyword(s):  
Technology Acceptance ◽  
United Arab Emirates ◽  
Technology Acceptance Model ◽  
Structural Equation ◽  
Systems Design ◽  
Equation Modeling ◽  
Research Model ◽  
Erp Systems ◽  
Erp System ◽  
Acceptance Model

The implementation of Enterprise Recourse Planning (ERP) systems has grown rapidly, but limited research has been conducted to investigate the utilization of ERP systems. By extending the Technology Acceptance Model, this paper provides a research model for examining the impact of computer self-efficacy and ERP systems design features on the utilization of ERP systems. To test the proposed research model, data are collected through a questionnaire survey distributed among employees in different organizations that have implemented an ERP system in the United Arab Emirates. Structural equation modeling techniques are used in this study to verify the causal relationships between the variables. The results strongly support the extended TAM in understanding employees’ utilization of ERP systems. The implications of this study and further research opportunities are also discussed.

scholarly journals Understanding the Quality Determinants that Influence the Intention to Use the Mobile Learning Platforms: A Practical Study

2019 ◽  
Vol 13 (11) ◽  
pp. 157 ◽  
Author(s):  
Muhammad Turki Alshurideh ◽  
Said Abdelrahim Salloum ◽  
Barween Al Kurdi ◽  
Azza Abdel Monem ◽  
Khaled Shaalan
Keyword(s):  
Technology Acceptance ◽  
United Arab Emirates ◽  
Mobile Learning ◽  
Structural Equation ◽  
Online Survey ◽  
Structural Model ◽  
Learning Experience ◽  
Internet Technology ◽  
Equation Modeling ◽  
Learning Platform

<p class="0abstract">There is a widespread use of Internet technology in the present times, because of which universities are making investments in Mobile learning to augment their position in the face of extensive competition and also to enhance their students’ learning experience and efficiency. Nonetheless, Mobile Learning Platform are only going to be successful when students show acceptance and adoption of this technology. Our literature review indicates that very few studies have been carried out to show how university students accept and employ Mobile Learning Platform. In addition, it is asserted that behavioral models of technology acceptance are not equally applied in different cultures. The purpose of this study is to develop an extension of Technology Acceptance Model (TAM) by including four more constructs: namely, content quality, service quality, information quality and quality of the system. This is proposed to make it more relevant for the developing countries, like the United Arab Emirates (UAE). An online survey was carried out to obtain the data. A total of 221 students from the UAE took part in this survey. Structural equation modeling was used to determine and test the measurement and structural model. Data analysis was carried out, which showed that ten out of a total of 12 hypotheses are supported. This shows that there is support for the applicability of the extended TAM in the UAE. These outcomes suggest that Mobile Learning Platform should be considered by the policymakers and education developers as being not only a technological solution but also as being new e-learning platform especially for distance learning students.</p>

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

1290-P: Gut Microbiota in New-Onset Pediatric Patients with Type 1 Diabetes: Machine Learning Algorithms to Classify Microorganisms Disease-Linked

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1290-P
Author(s):  
GIUSEPPE D’ANNUNZIO ◽  
ROBERTO BIASSONI ◽  
MARGHERITA SQUILLARIO ◽  
ELISABETTA UGOLOTTI ◽  
ANNALISA BARLA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 1 Diabetes ◽  
Gut Microbiota ◽  
Pediatric Patients ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Onset
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