Pub Date : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664755
Kainat Rizwan, Sehar Babar, Sania Nayab, M. Hanif
Cybersecurity has a great deal of importance over the digital market for organizations in this modern era. Nowadays all kinds of communications and connections are established by using the internet. Chatting is a main source of communication. The major problem faced by users is harassment. User starts to get harassed frequently as most of users does not know what to do and how to take action or how to stop this. In this work, we employ machine learning and natural language processing to tackle online harassment. This study proposed a real time machine learning based algorithm which detects harassment actively and alert user to take action against it. For detection mechanism, Naïve Bayes classification is used. The proposed approach attain approximately 77% accuracy. The result shows that the algorithm actively detects harassing keywords in chat messages.
{"title":"HarX: Real-time harassment detection tool using machine learning","authors":"Kainat Rizwan, Sehar Babar, Sania Nayab, M. Hanif","doi":"10.1109/MTICTI53925.2021.9664755","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664755","url":null,"abstract":"Cybersecurity has a great deal of importance over the digital market for organizations in this modern era. Nowadays all kinds of communications and connections are established by using the internet. Chatting is a main source of communication. The major problem faced by users is harassment. User starts to get harassed frequently as most of users does not know what to do and how to take action or how to stop this. In this work, we employ machine learning and natural language processing to tackle online harassment. This study proposed a real time machine learning based algorithm which detects harassment actively and alert user to take action against it. For detection mechanism, Naïve Bayes classification is used. The proposed approach attain approximately 77% accuracy. The result shows that the algorithm actively detects harassing keywords in chat messages.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126854386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664767
Mokhtar A. Al-Awadhi, R. Deshmukh
Honey has been a target for adulteration with various inexpensive industrial sugars. Discriminating between authentic and adulterated honey is a challenging problem for consumers. Several studies have proposed different methods for detecting adulterated honey. Traditional methods, such as stable carbon isotope ratio analysis, chromatography, and physicochemical parameter analysis, provided good qualitative and quantitative detection. These technologies utilize different approaches, such as profiles of honey constituents, physical and chemical properties of honey, and specific marker traces for the sugar adulterants. Spectroscopy and hyperspectral imaging provided fast and nondestructive detection with no sample preparation. Sensory techniques, such as low-cost optic fiber sensors, demonstrated their effectiveness in quantifying honey adulteration. This paper discusses various technologies for detecting and quantifying honey adulteration. We also discuss the machine learning models and their performance in this research.
{"title":"A Review on Modern Analytical Methods for Detecting and Quantifying Adulteration in Honey","authors":"Mokhtar A. Al-Awadhi, R. Deshmukh","doi":"10.1109/MTICTI53925.2021.9664767","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664767","url":null,"abstract":"Honey has been a target for adulteration with various inexpensive industrial sugars. Discriminating between authentic and adulterated honey is a challenging problem for consumers. Several studies have proposed different methods for detecting adulterated honey. Traditional methods, such as stable carbon isotope ratio analysis, chromatography, and physicochemical parameter analysis, provided good qualitative and quantitative detection. These technologies utilize different approaches, such as profiles of honey constituents, physical and chemical properties of honey, and specific marker traces for the sugar adulterants. Spectroscopy and hyperspectral imaging provided fast and nondestructive detection with no sample preparation. Sensory techniques, such as low-cost optic fiber sensors, demonstrated their effectiveness in quantifying honey adulteration. This paper discusses various technologies for detecting and quantifying honey adulteration. We also discuss the machine learning models and their performance in this research.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128999912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664754
Youness Chaabi, Yahya Al-Ashmoery
Persistence in online courses remains a concern for various institutions. The case of (Massive Open Online Courses) MOOCs represents a particular situation of online courses with an even higher dropout rate. This type of training highlights problems that have already been identified such as sociological isolation of the learner, loss of motivation, empowerment of the learner, acquisition of identity within a group and appreciation of the group pedagogical progress. In this context, the importance of a follow-up by a human tutor is unanimously recognized by the different a ctors. Despite the numerous services offered by open and distance learning platforms, one of the main difficulties encountered by tutors in this task is to have a sufficient understanding of what the distant learners are doing. One way that seems particularly promising to solve this problem is the exploitation of interaction traces left by learners within MOOCs, and the elaboration of indicators that can help the tutor in monitoring learners’ activities. This is why it seems imperative to me to propose a tool to visualize the work accomplished by each learner. The system must provide indicators that help the tutor to appreciate the work of the learners.
{"title":"Development of a Learning Analytics extension in Open edX","authors":"Youness Chaabi, Yahya Al-Ashmoery","doi":"10.1109/MTICTI53925.2021.9664754","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664754","url":null,"abstract":"Persistence in online courses remains a concern for various institutions. The case of (Massive Open Online Courses) MOOCs represents a particular situation of online courses with an even higher dropout rate. This type of training highlights problems that have already been identified such as sociological isolation of the learner, loss of motivation, empowerment of the learner, acquisition of identity within a group and appreciation of the group pedagogical progress. In this context, the importance of a follow-up by a human tutor is unanimously recognized by the different a ctors. Despite the numerous services offered by open and distance learning platforms, one of the main difficulties encountered by tutors in this task is to have a sufficient understanding of what the distant learners are doing. One way that seems particularly promising to solve this problem is the exploitation of interaction traces left by learners within MOOCs, and the elaboration of indicators that can help the tutor in monitoring learners’ activities. This is why it seems imperative to me to propose a tool to visualize the work accomplished by each learner. The system must provide indicators that help the tutor to appreciate the work of the learners.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"777 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133005113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664757
Arian Yousefiankalareh, Taraneh Kamyab, Ali Mojarrad Ghahfarokhi, Fatemehalsadat Beheshtinejad, Hossein Mirzanejad, Shahaboddin Seddighi
Generally, using collective intelligence is one of the interesting topics is researchers of recent years, which its purpose is modeling creatures’ simple behaviors and their interaction with the environment and neighbor creatures to obtain more complex behaviors. We could utilize algorithms based on collective intelligence to solve complicated problems like optimization problems. So far, various algorithms have been purposed in this field which firefly algorithm is a variant of these. In this algorithm, each member acts as a better response concerning itself. However, this algorithm has some drawbacks like the consistency of parameters value, lack of balance between local search and global search, and others. On one hand, multi-agent systems are software systems that contain sets of agents. These agents perform their tasks together to solve a problem and reach the desired purpose. In this paper, we have tried to utilize a multi-agent system, in addition to meta-heuristic optimization algorithms, to improve the performance of the firefly algorithm to better cooperate warms populations with each other. The results of the experiment show the acceptable performance of the proposed algorithm.
{"title":"Utilizing Multi-Agent Systems Approach in Firefly Algorithm","authors":"Arian Yousefiankalareh, Taraneh Kamyab, Ali Mojarrad Ghahfarokhi, Fatemehalsadat Beheshtinejad, Hossein Mirzanejad, Shahaboddin Seddighi","doi":"10.1109/MTICTI53925.2021.9664757","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664757","url":null,"abstract":"Generally, using collective intelligence is one of the interesting topics is researchers of recent years, which its purpose is modeling creatures’ simple behaviors and their interaction with the environment and neighbor creatures to obtain more complex behaviors. We could utilize algorithms based on collective intelligence to solve complicated problems like optimization problems. So far, various algorithms have been purposed in this field which firefly algorithm is a variant of these. In this algorithm, each member acts as a better response concerning itself. However, this algorithm has some drawbacks like the consistency of parameters value, lack of balance between local search and global search, and others. On one hand, multi-agent systems are software systems that contain sets of agents. These agents perform their tasks together to solve a problem and reach the desired purpose. In this paper, we have tried to utilize a multi-agent system, in addition to meta-heuristic optimization algorithms, to improve the performance of the firefly algorithm to better cooperate warms populations with each other. The results of the experiment show the acceptable performance of the proposed algorithm.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121499620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664784
Dana F. Doghramachi, S. Ameen
Nowadays, the Internet of Things (IoT) applications will increase rapidly, such as smart cities, smart transportation, smart healthcare, and smart things are all composed of the IoT, which connects a wide range of heterogeneous devices. This review paper provides a comprehensive overview of blockchain architectures designed for IoT networks. The IoT has emerged as a sector with enormous impact, potential, and growth, with billions of devices expected to connect to the Internet in the coming years. The first big challenge we will be faced with this big network is security; it must be taken into account. In contrast to other endpoint devices such as smartphones, laptops, and PCs, most IoT devices are more sensitive to assaults. Because of the various specifications and heterogeneity problems, traditional protection primitives cannot be explicitly applied to IoT technologies. This paper investigates these critical and essential issues regarding IoT threats, security requirements, and challenges with particular emphasis on and relation to IoT layered architecture. Next, the paper addresses various existing IoT security technologies to achieve high security for IoT applications. It also addresses how two methods, Blockchain, and context-aware, can help solve many IoT security issues. Finally, the paper assesses and suggests some recommendations for future research in the field of IoT security.
如今，物联网(Internet of Things, IoT)的应用将迅速增加，智慧城市、智慧交通、智慧医疗、智能物联网等都是由物联网组成的，物联网连接了各种各样的异构设备。这篇综述文章提供了为物联网网络设计的区块链架构的全面概述。物联网已经成为一个具有巨大影响、潜力和增长的领域，预计未来几年将有数十亿设备连接到互联网。面对这个庞大的网络，我们面临的第一个重大挑战是安全;这一点必须加以考虑。与智能手机、笔记本电脑和个人电脑等其他端点设备相比，大多数物联网设备对攻击更敏感。由于各种规格和异构问题，传统的保护原语不能明确地应用于物联网技术。本文研究了关于物联网威胁、安全要求和挑战的这些关键和基本问题，特别强调了物联网分层架构及其关系。接下来，本文介绍了现有的各种物联网安全技术，以实现物联网应用的高安全性。它还解决了区块链和上下文感知两种方法如何帮助解决许多物联网安全问题。最后，本文对物联网安全领域的未来研究进行了评估和建议。
{"title":"IoT Threats and Solutions with Blockchain and Context-Aware Security Design: A Review","authors":"Dana F. Doghramachi, S. Ameen","doi":"10.1109/MTICTI53925.2021.9664784","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664784","url":null,"abstract":"Nowadays, the Internet of Things (IoT) applications will increase rapidly, such as smart cities, smart transportation, smart healthcare, and smart things are all composed of the IoT, which connects a wide range of heterogeneous devices. This review paper provides a comprehensive overview of blockchain architectures designed for IoT networks. The IoT has emerged as a sector with enormous impact, potential, and growth, with billions of devices expected to connect to the Internet in the coming years. The first big challenge we will be faced with this big network is security; it must be taken into account. In contrast to other endpoint devices such as smartphones, laptops, and PCs, most IoT devices are more sensitive to assaults. Because of the various specifications and heterogeneity problems, traditional protection primitives cannot be explicitly applied to IoT technologies. This paper investigates these critical and essential issues regarding IoT threats, security requirements, and challenges with particular emphasis on and relation to IoT layered architecture. Next, the paper addresses various existing IoT security technologies to achieve high security for IoT applications. It also addresses how two methods, Blockchain, and context-aware, can help solve many IoT security issues. Finally, the paper assesses and suggests some recommendations for future research in the field of IoT security.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115098629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664777
Sarah Amjad Inad, Khattab M. Ali Alheeti, S. S. Al-Rawi
Wireless Sensor Networks (WSNs) consist of small devices that sense environmental and physical phenomena. WSNs have proven their high capabilities in various fields, but there are some challenges facing sensor nodes, such as limited battery power and their communication and storage capabilities. This review of recent literature on energy improvement strategies is compressed in this review paper. The concepts of WSNs, power sources, WSNs node component, Energy Saving Techniques, power consumption were extracted and clarified. Finally, the results of previous studies regarding energy improvement were extracted, discussed, and compared at the end of the paper.
{"title":"Energy Conservation Strategies in Wireless Sensor Networks: A Review","authors":"Sarah Amjad Inad, Khattab M. Ali Alheeti, S. S. Al-Rawi","doi":"10.1109/MTICTI53925.2021.9664777","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664777","url":null,"abstract":"Wireless Sensor Networks (WSNs) consist of small devices that sense environmental and physical phenomena. WSNs have proven their high capabilities in various fields, but there are some challenges facing sensor nodes, such as limited battery power and their communication and storage capabilities. This review of recent literature on energy improvement strategies is compressed in this review paper. The concepts of WSNs, power sources, WSNs node component, Energy Saving Techniques, power consumption were extracted and clarified. Finally, the results of previous studies regarding energy improvement were extracted, discussed, and compared at the end of the paper.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116743872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/mticti53925.2021.9664768
{"title":"[MTICTI 2021 Front cover]","authors":"","doi":"10.1109/mticti53925.2021.9664768","DOIUrl":"https://doi.org/10.1109/mticti53925.2021.9664768","url":null,"abstract":"","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123549739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664782
Marwa Mohammed Khalifa, O. Ucan, Khattab M. Ali Alheeti
The Intrusion Detection System (IDS) is one of the technologies available to protect mobile ad hoc networks. The system monitors the network and detects intrusion from malicious nodes, aiming at passive (eavesdropping) or positive attack to disrupt the network. This paper proposes a new Intrusion detection system using three Machine Learning (ML) techniques. The ML techniques were Random Forest (RF), support vector machines (SVM), and Naïve Bayes(NB) were used to classify nodes in MANET. The data set was generated by the simulator network simulator-2 (NS-2). The routing protocol was used is Dynamic Source Routing (DSR). The type of IDS used is a Network Intrusion Detection System (NIDS). The dataset was pre-processed, then split into two subsets, 67% for training and 33% for testing employing Python Version 3.8.8. Obtaining good results for RF, SVM and NB when applied randomly selected features in the trial and error method from the dataset to improve the performance of the IDS and reduce time spent for training and testing. The system showed promising results, especially with RF, where the accuracy rate reached 100%.
{"title":"New Intrusion Detection System to Protect MANET Networks Employing Machine Learning Techniques","authors":"Marwa Mohammed Khalifa, O. Ucan, Khattab M. Ali Alheeti","doi":"10.1109/MTICTI53925.2021.9664782","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664782","url":null,"abstract":"The Intrusion Detection System (IDS) is one of the technologies available to protect mobile ad hoc networks. The system monitors the network and detects intrusion from malicious nodes, aiming at passive (eavesdropping) or positive attack to disrupt the network. This paper proposes a new Intrusion detection system using three Machine Learning (ML) techniques. The ML techniques were Random Forest (RF), support vector machines (SVM), and Naïve Bayes(NB) were used to classify nodes in MANET. The data set was generated by the simulator network simulator-2 (NS-2). The routing protocol was used is Dynamic Source Routing (DSR). The type of IDS used is a Network Intrusion Detection System (NIDS). The dataset was pre-processed, then split into two subsets, 67% for training and 33% for testing employing Python Version 3.8.8. Obtaining good results for RF, SVM and NB when applied randomly selected features in the trial and error method from the dataset to improve the performance of the IDS and reduce time spent for training and testing. The system showed promising results, especially with RF, where the accuracy rate reached 100%.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123837598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664764
Mokhtar A. Al-Awadhi, R. Deshmukh
In this paper, we propose a system for detecting adulteration in coconut milk, utilizing infrared spectroscopy. The machine learning-based proposed system comprises three phases: preprocessing, feature extraction, and classification. The first phase involves removing irrelevant data from coconut milk spectral signals. In the second phase, we employ the Linear Discriminant Analysis (LDA) algorithm for extracting the most discriminating features. In the third phase, we use the K-Nearest Neighbor (KNN) model to classify coconut milk samples into authentic or adulterated. We evaluate the performance of the proposed system using a public dataset comprising Fourier Transform Infrared (FTIR) spectral information of pure and contaminated coconut milk samples. Findings show that the proposed method successfully detects adulteration with a cross-validation accuracy of 93.33%.
{"title":"Detection of Adulteration in Coconut Milk using Infrared Spectroscopy and Machine Learning","authors":"Mokhtar A. Al-Awadhi, R. Deshmukh","doi":"10.1109/MTICTI53925.2021.9664764","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664764","url":null,"abstract":"In this paper, we propose a system for detecting adulteration in coconut milk, utilizing infrared spectroscopy. The machine learning-based proposed system comprises three phases: preprocessing, feature extraction, and classification. The first phase involves removing irrelevant data from coconut milk spectral signals. In the second phase, we employ the Linear Discriminant Analysis (LDA) algorithm for extracting the most discriminating features. In the third phase, we use the K-Nearest Neighbor (KNN) model to classify coconut milk samples into authentic or adulterated. We evaluate the performance of the proposed system using a public dataset comprising Fourier Transform Infrared (FTIR) spectral information of pure and contaminated coconut milk samples. Findings show that the proposed method successfully detects adulteration with a cross-validation accuracy of 93.33%.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124287668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/MTICTI53925.2021.9664769
Saadia Anwar Pasha, E. Youssef, Humaira Sharif
This study focuses on examining the role of Virtual Reality in improving the students’ educational experiences through Learning Management System in Pakistan. The researchers adopted a cross-sectional design and analyzed the gathered data by using survey questionnaires. By using the SEM, findings indicated a strong, significant relationship between Virtual Reality, Expectation Confirmation (p < 0.000), Attitude (p < 0.000), and Knowledge Acquisition (p < 0.000. However, the relationship between Behavioral Intention, Expectation Confirmation (p < 0.451) and Attitude (p < 0.161) remained insignificant. On the other hand, the proposed relationship between Knowledge Acquisition and Behavioral was strongly significant, with the p-value at 0.000. Finally, results revealed a strong, significant relationship between Behavioral Intention and Improved Learning Management System Experiences with the t-value at 10.474 and p-value at 0.000. Thus, this article concluded that it is important to develop and incorporate Virtual and Augmented Reality in education. Especially when the students depend on digital learning platforms, Virtual Reality adoption improves their learning experiences.
{"title":"Role of Virtual Reality in Improving Students’ LMS Experiences: Structural Equation Modelling Based Study","authors":"Saadia Anwar Pasha, E. Youssef, Humaira Sharif","doi":"10.1109/MTICTI53925.2021.9664769","DOIUrl":"https://doi.org/10.1109/MTICTI53925.2021.9664769","url":null,"abstract":"This study focuses on examining the role of Virtual Reality in improving the students’ educational experiences through Learning Management System in Pakistan. The researchers adopted a cross-sectional design and analyzed the gathered data by using survey questionnaires. By using the SEM, findings indicated a strong, significant relationship between Virtual Reality, Expectation Confirmation (p < 0.000), Attitude (p < 0.000), and Knowledge Acquisition (p < 0.000. However, the relationship between Behavioral Intention, Expectation Confirmation (p < 0.451) and Attitude (p < 0.161) remained insignificant. On the other hand, the proposed relationship between Knowledge Acquisition and Behavioral was strongly significant, with the p-value at 0.000. Finally, results revealed a strong, significant relationship between Behavioral Intention and Improved Learning Management System Experiences with the t-value at 10.474 and p-value at 0.000. Thus, this article concluded that it is important to develop and incorporate Virtual and Augmented Reality in education. Especially when the students depend on digital learning platforms, Virtual Reality adoption improves their learning experiences.","PeriodicalId":218225,"journal":{"name":"2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125532325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：