Pub Date : 2023-08-03DOI: 10.1109/icABCD59051.2023.10220456
H. Orovwode, Ibukun Deborah Oduntan, J. Abubakar
Deafness and voice impairment have been persistent disabilities throughout history, hindering individuals from engaging in verbal communication and leading to their isolation from the predominantly vocally communicating society. Sign language has emerged as the primary mode of communication for people with these disabilities. However, it presents a language barrier as it is not commonly understood by those who can hear. To address this issue, various methods for recognizing sign language have been proposed. This paperaims to develop a machine learning-based system that can recognize sign language in real-time. The paper involved the acquisition of a dataset consisting of 44,654 images representing the static American Sign Language (ASL) alphabet signs. The HandDetector module was utilized to detect and capture images of the signer's hand forming each sign through a PC webcam. The dataset was split into three sets: training data (20,772 cases), validation data (8,903 cases), and test data (14,979 cases). Image pre-processing techniques were implemented on the images and a convolutional neural network (CNN) model was trained and compiled. The CNN utilized in the paper comprised of three convolutional layers and a SoftMax output layer and it was compiled using the Adam optimizer and categorical cross-entropy loss function. The performance of the system was evaluated using the test dataset. Notably, the system achieved remarkable accuracy rates, having a training accuracy of 99.86%, a validation accuracy of 99.94%, and a test accuracy of 94.68%. The results obtained from this study demonstrated significant advancements in sign language recognition, surpassing previous findings in the literature.
{"title":"Development of a Sign Language Recognition System Using Machine Learning","authors":"H. Orovwode, Ibukun Deborah Oduntan, J. Abubakar","doi":"10.1109/icABCD59051.2023.10220456","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220456","url":null,"abstract":"Deafness and voice impairment have been persistent disabilities throughout history, hindering individuals from engaging in verbal communication and leading to their isolation from the predominantly vocally communicating society. Sign language has emerged as the primary mode of communication for people with these disabilities. However, it presents a language barrier as it is not commonly understood by those who can hear. To address this issue, various methods for recognizing sign language have been proposed. This paperaims to develop a machine learning-based system that can recognize sign language in real-time. The paper involved the acquisition of a dataset consisting of 44,654 images representing the static American Sign Language (ASL) alphabet signs. The HandDetector module was utilized to detect and capture images of the signer's hand forming each sign through a PC webcam. The dataset was split into three sets: training data (20,772 cases), validation data (8,903 cases), and test data (14,979 cases). Image pre-processing techniques were implemented on the images and a convolutional neural network (CNN) model was trained and compiled. The CNN utilized in the paper comprised of three convolutional layers and a SoftMax output layer and it was compiled using the Adam optimizer and categorical cross-entropy loss function. The performance of the system was evaluated using the test dataset. Notably, the system achieved remarkable accuracy rates, having a training accuracy of 99.86%, a validation accuracy of 99.94%, and a test accuracy of 94.68%. The results obtained from this study demonstrated significant advancements in sign language recognition, surpassing previous findings in the literature.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"15 1","pages":"1-8"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82770967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-03DOI: 10.1109/icABCD59051.2023.10220532
Feliciana M. E. Manuel, S. Saide, Felermino M. D. A. Ali, Sanae Lotfi
Ocular cataract is among diseases that result in blindness if not treated in time. It affects people worldwide, primarily in underdeveloped countries. This health problem affects the quality of patients' lives. However, early diagnosis avoids blindness and allows the patient to have appropriate treatment. Developing countries, especially those with low income, have a precarious health system, even in the ophthalmology sector, where equipment is lacking. This research aims to develop a deep learning-based model to detect ocular cataracts based on retinal images. We collect 1000 retinal images from Kaggle, which are then equally divided into two classes: with and without cataracts. We then use several neural architectures to correctly classify these images, including ResNet18, ResNet34, InceptionResNetV2, and InceptionV4. We demonstrate that ResNet18 outperforms the other architectures, reaching 95.5% accuracy score. Our results suggest that deep convolutional neural networks can achieve a significant performance in ocular cataracts classification using retinal images.
{"title":"Ocular Cataract Identification Using Deep Convolutional Neural Networks","authors":"Feliciana M. E. Manuel, S. Saide, Felermino M. D. A. Ali, Sanae Lotfi","doi":"10.1109/icABCD59051.2023.10220532","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220532","url":null,"abstract":"Ocular cataract is among diseases that result in blindness if not treated in time. It affects people worldwide, primarily in underdeveloped countries. This health problem affects the quality of patients' lives. However, early diagnosis avoids blindness and allows the patient to have appropriate treatment. Developing countries, especially those with low income, have a precarious health system, even in the ophthalmology sector, where equipment is lacking. This research aims to develop a deep learning-based model to detect ocular cataracts based on retinal images. We collect 1000 retinal images from Kaggle, which are then equally divided into two classes: with and without cataracts. We then use several neural architectures to correctly classify these images, including ResNet18, ResNet34, InceptionResNetV2, and InceptionV4. We demonstrate that ResNet18 outperforms the other architectures, reaching 95.5% accuracy score. Our results suggest that deep convolutional neural networks can achieve a significant performance in ocular cataracts classification using retinal images.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"259 1","pages":"1-5"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77104460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-03DOI: 10.1109/icABCD59051.2023.10220534
Mark Marais, Dane Brown, James Connan, Alden Boby
Sign language is a vital tool of communication for individuals who are deaf or hard of hearing. Sign language recognition (SLR) technology can assist in bridging the communication gap between deaf and hearing individuals. However, existing SLR systems are typically signer-dependent, requiring training data from the specific signer for accurate recognition. This presents a significant challenge for practical use, as collecting data from every possible signer is not feasible. This research focuses on developing a signer-independent isolated SLR system to address this challenge. The system implements two model variants on the signer-independent datasets: an R(2+ I)D spatiotemporal convolutional block and a Video Vision transformer. These models learn to extract features from raw sign language videos from the LSA64 dataset and classify signs without needing handcrafted features, explicit segmentation or pose estimation. Overall, the R(2+1)D model architecture significantly outperformed the ViViT architecture for signer-independent SLR on the LSA64 dataset. The R(2+1)D model achieved a near-perfect accuracy of 99.53% on the unseen test set, with the ViViT model yielding an accuracy of 72.19 %. Proving that spatiotemporal convolutions are effective at signer-independent SLR.
{"title":"Spatiotemporal Convolutions and Video Vision Transformers for Signer-Independent Sign Language Recognition","authors":"Mark Marais, Dane Brown, James Connan, Alden Boby","doi":"10.1109/icABCD59051.2023.10220534","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220534","url":null,"abstract":"Sign language is a vital tool of communication for individuals who are deaf or hard of hearing. Sign language recognition (SLR) technology can assist in bridging the communication gap between deaf and hearing individuals. However, existing SLR systems are typically signer-dependent, requiring training data from the specific signer for accurate recognition. This presents a significant challenge for practical use, as collecting data from every possible signer is not feasible. This research focuses on developing a signer-independent isolated SLR system to address this challenge. The system implements two model variants on the signer-independent datasets: an R(2+ I)D spatiotemporal convolutional block and a Video Vision transformer. These models learn to extract features from raw sign language videos from the LSA64 dataset and classify signs without needing handcrafted features, explicit segmentation or pose estimation. Overall, the R(2+1)D model architecture significantly outperformed the ViViT architecture for signer-independent SLR on the LSA64 dataset. The R(2+1)D model achieved a near-perfect accuracy of 99.53% on the unseen test set, with the ViViT model yielding an accuracy of 72.19 %. Proving that spatiotemporal convolutions are effective at signer-independent SLR.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"7 1","pages":"1-6"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80853503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-03DOI: 10.1109/icABCD59051.2023.10220474
Shingirirai M. Chakoma, K. Ogudo
The millimeter-wave (mmWave) frequency band is rapidly becoming utilized in wireless technologies due to its large bandwidth and high data throughput. Wireless technology is increasingly becoming the backbone of the Internet of Things (IoT). This has resulted in increased applications of the radio frequency (RF) spectrum and congestion of the microwave band. This can be solved by utilizing more bandwidth at higher frequency bands. One notable application of IoT pertains to radar sensing, which has experienced increased popularity across various domains such as autonomous vehicles, gesture recognition, drones, and health monitoring. Radar sensors have been employed in these applications to perform tasks including proximity sensing, direction detection, speed measurement, target localization, and capturing physiological indicators such as heartbeat and breathing. Several factors have an impact on the performance of radar sensors, encompassing the maximum range for target detection, measurement precision, capability to differentiate between multiple targets, and ability to operate effectively in environments with high levels of noise. This paper presents the design of a 45 nm complementary metal-oxide-semiconductor (CMOS) low noise amplifier (LNA) for a mmWave Ka-band wireless transceiver for radar sensors. The LNA was designed to operate at 0.6V and 700 μA for low power consumption. The LNA consists of an inductive degenerated common source (CS) and a common gate (CG) diode-connected load. The LNA achieves a power gain of 31.19 dB and a noise figure (NF) of 0.133 dB at 30 GHz consuming 0.42 mW of power.
{"title":"Design of a 45 nm Complementary Metal Oxide Semiconductor Low Noise Amplifier for a 30 GHz Millimeter-Wave Wireless Transceiver in Radar Sensor Applications","authors":"Shingirirai M. Chakoma, K. Ogudo","doi":"10.1109/icABCD59051.2023.10220474","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220474","url":null,"abstract":"The millimeter-wave (mmWave) frequency band is rapidly becoming utilized in wireless technologies due to its large bandwidth and high data throughput. Wireless technology is increasingly becoming the backbone of the Internet of Things (IoT). This has resulted in increased applications of the radio frequency (RF) spectrum and congestion of the microwave band. This can be solved by utilizing more bandwidth at higher frequency bands. One notable application of IoT pertains to radar sensing, which has experienced increased popularity across various domains such as autonomous vehicles, gesture recognition, drones, and health monitoring. Radar sensors have been employed in these applications to perform tasks including proximity sensing, direction detection, speed measurement, target localization, and capturing physiological indicators such as heartbeat and breathing. Several factors have an impact on the performance of radar sensors, encompassing the maximum range for target detection, measurement precision, capability to differentiate between multiple targets, and ability to operate effectively in environments with high levels of noise. This paper presents the design of a 45 nm complementary metal-oxide-semiconductor (CMOS) low noise amplifier (LNA) for a mmWave Ka-band wireless transceiver for radar sensors. The LNA was designed to operate at 0.6V and 700 μA for low power consumption. The LNA consists of an inductive degenerated common source (CS) and a common gate (CG) diode-connected load. The LNA achieves a power gain of 31.19 dB and a noise figure (NF) of 0.133 dB at 30 GHz consuming 0.42 mW of power.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"35 4 1","pages":"1-7"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79166773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-03DOI: 10.1109/icABCD59051.2023.10220453
John Batani
The death of children before they reach five years old (under-five mortality or U5M) is a global scourge that has attracted the attention of many governments, including the World Health Organisation and the United Nations. Children under-five in Sub-Saharan Africa are disproportionately susceptible to death, with a fifteen-fold likelihood of death compared to their counterparts in developed countries. Regardless of the numerous efforts by the Zimbabwean Government to improve child health, such as free access to care, provision of nutritional supplements, immunisation programmes and prevention of mother-to-child transmission, the country still has high under-five mortality rates (U5MRs). Zimbabwe's failure to reduce U5MRs to acceptable levels suggests that the current methods must be complemented. Identifying contextual risk factors and children at risk of death could help paediatricians to make timely and targeted interventions and policymakers to review existing and craft new policies to save children's lives. Therefore, this study applied deep learning to Zimbabwe's 2019 Multiple Indicator Cluster Survey data to predict under-five mortality and identify its associated risk factors. The study used a deep neural network with four hidden layers, k-fold cross-validation and the stochastic gradient descent (SGD) optimiser. All layers used the Rectified Linear Unit activation function except the output layer, which used the sigmoid activation for binary classification. The model produced a 90.04% accuracy, 92.39% precision, 87.30% recall and 95.04% area under the curve. Though the model predicts under-five mortality, it does not prescribe the appropriate interventions to save lives, a gap that future studies could fill.
五岁以下儿童死亡(五岁以下儿童死亡或U5M)是一个全球性的灾难，已引起包括世界卫生组织和联合国在内的许多政府的关注。撒哈拉以南非洲五岁以下儿童特别容易死亡，其死亡可能性是发达国家儿童的15倍。尽管津巴布韦政府为改善儿童健康作出了许多努力，例如免费获得护理、提供营养补充、免疫方案和预防母婴传播，但该国五岁以下儿童死亡率仍然很高。津巴布韦未能将5岁以下儿童死亡率降低到可接受的水平，这表明必须补充当前的方法。确定环境风险因素和面临死亡风险的儿童可以帮助儿科医生及时采取有针对性的干预措施，也可以帮助决策者审查现有政策并制定新的政策，以挽救儿童的生命。因此，本研究将深度学习应用于津巴布韦2019年的多指标聚类调查数据，以预测五岁以下儿童死亡率并确定其相关风险因素。该研究使用了具有四个隐藏层的深度神经网络，k-fold交叉验证和随机梯度下降(SGD)优化器。除了输出层使用sigmoid激活进行二值分类外，所有层都使用了Rectified Linear Unit激活函数。该模型的准确率为90.04%，精密度为92.39%，召回率为87.30%，曲线下面积为95.04%。虽然该模型预测了五岁以下儿童的死亡率，但它并没有规定适当的干预措施来挽救生命，这是未来研究可以填补的空白。
{"title":"A Deep Learning Model for Predicting Under-Five Mortality in Zimbabwe","authors":"John Batani","doi":"10.1109/icABCD59051.2023.10220453","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220453","url":null,"abstract":"The death of children before they reach five years old (under-five mortality or U5M) is a global scourge that has attracted the attention of many governments, including the World Health Organisation and the United Nations. Children under-five in Sub-Saharan Africa are disproportionately susceptible to death, with a fifteen-fold likelihood of death compared to their counterparts in developed countries. Regardless of the numerous efforts by the Zimbabwean Government to improve child health, such as free access to care, provision of nutritional supplements, immunisation programmes and prevention of mother-to-child transmission, the country still has high under-five mortality rates (U5MRs). Zimbabwe's failure to reduce U5MRs to acceptable levels suggests that the current methods must be complemented. Identifying contextual risk factors and children at risk of death could help paediatricians to make timely and targeted interventions and policymakers to review existing and craft new policies to save children's lives. Therefore, this study applied deep learning to Zimbabwe's 2019 Multiple Indicator Cluster Survey data to predict under-five mortality and identify its associated risk factors. The study used a deep neural network with four hidden layers, k-fold cross-validation and the stochastic gradient descent (SGD) optimiser. All layers used the Rectified Linear Unit activation function except the output layer, which used the sigmoid activation for binary classification. The model produced a 90.04% accuracy, 92.39% precision, 87.30% recall and 95.04% area under the curve. Though the model predicts under-five mortality, it does not prescribe the appropriate interventions to save lives, a gap that future studies could fill.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"195 1","pages":"1-6"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77115425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-03DOI: 10.1109/icABCD59051.2023.10220571
Patrick Mwansa, Boniface Kabaso
Inconsistencies, unclear processes, and procedures in most democratic countries' election management, particularly vote-counting, cause mistrust and dispute in election results. This study explores the perceptions and expectations of electoral stakeholders on vote counting and validation processes in different African countries. Employing thematic analysis and using the Activity Theory as a lens, the research identifies key themes, such as technical aspects, accuracy, speed, efficiency, transparency, security, challenges, improvements, and the roles of observers and Election Management Bodies (EMBs). The findings highlight various challenges, such as poor network coverage, insufficient staff training, and corruption, informing the formation of a requirement specification for vote counting and validation systems. Despite potential drawbacks and challenges associated with technology solutions, the study proposes a set of ideal requirements specifications for an accurate, efficient, transparent, and secure election vote counting and validation process. The study contributes to ongoing discussions on transparency, accessibility, and the use of electronic voting systems to enhance electoral accuracy and integrity. It suggests avenues for future research, including evaluating legal and regulatory frameworks, voter education, technical challenges, and alternative technological approaches to vote counting and validation.
{"title":"Perception and Expectations of Vote Counting and Validation Systems: A Survey of Electoral Stakeholders","authors":"Patrick Mwansa, Boniface Kabaso","doi":"10.1109/icABCD59051.2023.10220571","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220571","url":null,"abstract":"Inconsistencies, unclear processes, and procedures in most democratic countries' election management, particularly vote-counting, cause mistrust and dispute in election results. This study explores the perceptions and expectations of electoral stakeholders on vote counting and validation processes in different African countries. Employing thematic analysis and using the Activity Theory as a lens, the research identifies key themes, such as technical aspects, accuracy, speed, efficiency, transparency, security, challenges, improvements, and the roles of observers and Election Management Bodies (EMBs). The findings highlight various challenges, such as poor network coverage, insufficient staff training, and corruption, informing the formation of a requirement specification for vote counting and validation systems. Despite potential drawbacks and challenges associated with technology solutions, the study proposes a set of ideal requirements specifications for an accurate, efficient, transparent, and secure election vote counting and validation process. The study contributes to ongoing discussions on transparency, accessibility, and the use of electronic voting systems to enhance electoral accuracy and integrity. It suggests avenues for future research, including evaluating legal and regulatory frameworks, voter education, technical challenges, and alternative technological approaches to vote counting and validation.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"2 1","pages":"1-10"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89214510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-03DOI: 10.1109/icABCD59051.2023.10220488
Lesetja S. Mabunda, T. S. Hlalele
A web-based communication interface for home automation enables users to remotely control and monitor their home appliances and devices using a web browser. This technology has the potential to improve energy efficiency and convenience of homes, but also raises security and privacy concerns. This paper focus on the development of a web-based home automation communication interface employing the ThingSpeak platform as the cloud interfacing service, an Arduino board as the microcontroller, and the ESP8266 as the internet module. The subsystem components were tested using hardware testing circuits and a software code written using Arduino IDE to determine the standard operating procedure and results show that they all conform to the operating standards. When the PIR sensor was tested a voltage of 0 V was measured on the digital pin 2 of the Arduino when no motion is detected and a voltage of 3.25 V when motion is detected. The MQ7 sensor gave a reading of 1.73 V on the analog data pin when not exposed to CO (carbon monoxide) and a reading of 3.25 V when exposed to CO.
{"title":"Design and Construction of a Web-Based Communication Interface for Home Automation","authors":"Lesetja S. Mabunda, T. S. Hlalele","doi":"10.1109/icABCD59051.2023.10220488","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220488","url":null,"abstract":"A web-based communication interface for home automation enables users to remotely control and monitor their home appliances and devices using a web browser. This technology has the potential to improve energy efficiency and convenience of homes, but also raises security and privacy concerns. This paper focus on the development of a web-based home automation communication interface employing the ThingSpeak platform as the cloud interfacing service, an Arduino board as the microcontroller, and the ESP8266 as the internet module. The subsystem components were tested using hardware testing circuits and a software code written using Arduino IDE to determine the standard operating procedure and results show that they all conform to the operating standards. When the PIR sensor was tested a voltage of 0 V was measured on the digital pin 2 of the Arduino when no motion is detected and a voltage of 3.25 V when motion is detected. The MQ7 sensor gave a reading of 1.73 V on the analog data pin when not exposed to CO (carbon monoxide) and a reading of 3.25 V when exposed to CO.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"16 1","pages":"1-7"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88755506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-03DOI: 10.1109/icABCD59051.2023.10220568
S. Oguta, Akindele Akinyinka, S. Ojo, B. Maake
Gamification, a concept that describes the use of game design elements in non-game scenarios, inan attempt to induce fun and motivation in non-game scenarios, has found application in a variety of contexts. In education, popular gamification elements that have been introduced to make learning fun includes the use of points, levels, missions, leaderboards, badges, and avatars. New gamification such as the use of social robots is now in vogue. Despite the enormous potential of these gaming concepts, researchers have unearthed some challenges that face the adoption of gamification in educational systems of Higher Education Institutions. In this study's systematic literature review, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework was used to explore the difficulties encountered when using gamification in training and education at colleges and universities and tosuggest potential solutions. Several researches that employed gamification were elicited from databases such as google scholar, Scopus and research gate. The shortcomings encountered during the deployment of gamification in these studies were summarized. The study's findings reveal that he difficulties encountered when adopting gamification in education and training in higher education institutions can be divided into three categories: design concerns, issues with short-term engagement, and problems with user adaptability. Likewise, potential fixes for the issues were mapped out for future designers of educational gamified systems to follow.
{"title":"The Constraints of The Adoption of Gamification for Education and Training in Higher Education Institutions: A Systematic Literature Review","authors":"S. Oguta, Akindele Akinyinka, S. Ojo, B. Maake","doi":"10.1109/icABCD59051.2023.10220568","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220568","url":null,"abstract":"Gamification, a concept that describes the use of game design elements in non-game scenarios, inan attempt to induce fun and motivation in non-game scenarios, has found application in a variety of contexts. In education, popular gamification elements that have been introduced to make learning fun includes the use of points, levels, missions, leaderboards, badges, and avatars. New gamification such as the use of social robots is now in vogue. Despite the enormous potential of these gaming concepts, researchers have unearthed some challenges that face the adoption of gamification in educational systems of Higher Education Institutions. In this study's systematic literature review, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework was used to explore the difficulties encountered when using gamification in training and education at colleges and universities and tosuggest potential solutions. Several researches that employed gamification were elicited from databases such as google scholar, Scopus and research gate. The shortcomings encountered during the deployment of gamification in these studies were summarized. The study's findings reveal that he difficulties encountered when adopting gamification in education and training in higher education institutions can be divided into three categories: design concerns, issues with short-term engagement, and problems with user adaptability. Likewise, potential fixes for the issues were mapped out for future designers of educational gamified systems to follow.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"16 1","pages":"1-6"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89522631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","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：