Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00122
Herious A. Cotton, T. Kwembe
In this paper, we used data analytics to analyze criminal data. Prophet model, LSTM recurrent neural network model, a linear regression model, and traditional neural network model were used to predict homicide and rape in the Southeastern Cities of Memphis Tennessee, Jackson Mississippi, and New Orleans Louisiana. LSTM recurrent neural network model and traditional neural network model have smaller RMSE. Thus, LSTM recurrent neural network model and traditional neural network model performed better than the prophet and linear regression models. These promising outcomes will be significant to scholars, policymakers, and law enforcement officers.
{"title":"Using Data Analytics to Forecast Violent Crime","authors":"Herious A. Cotton, T. Kwembe","doi":"10.1109/CSCI54926.2021.00122","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00122","url":null,"abstract":"In this paper, we used data analytics to analyze criminal data. Prophet model, LSTM recurrent neural network model, a linear regression model, and traditional neural network model were used to predict homicide and rape in the Southeastern Cities of Memphis Tennessee, Jackson Mississippi, and New Orleans Louisiana. LSTM recurrent neural network model and traditional neural network model have smaller RMSE. Thus, LSTM recurrent neural network model and traditional neural network model performed better than the prophet and linear regression models. These promising outcomes will be significant to scholars, policymakers, and law enforcement officers.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128247325","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 : 2021-12-01DOI: 10.1109/CSCI54926.2021.00322
Yifeng Tan, Lina Yang, Xichun Li, Zuqiang Meng
Cardiac MRI image segmentation is of great importance for evaluating cardiac function and diagnosing diseases. Manual segmentation is time-consuming and tedious, so automatic segmentation is very popular in practical applications. In this paper, we propose an improved full convolutional neural network based on 2D-Unet for automatic segmentation of the left ventricle, right ventricle and myocardium. Experiments were conducted on the ACDC 2017 Challenge Training dataset. The segmentation results were assessed by means of average vertical distance, Dice coefficient and Hausdorff distance. Our model reduces the amount of parameters, improves the training speed, uses the fusion loss function, and maintains a satisfactory segmentation accuracy of left ventricle, right ventricle and myocardium.
{"title":"A Fully Convolutional Neural Network Based on 2D-Unet in Cardiac MR Image Segmentation","authors":"Yifeng Tan, Lina Yang, Xichun Li, Zuqiang Meng","doi":"10.1109/CSCI54926.2021.00322","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00322","url":null,"abstract":"Cardiac MRI image segmentation is of great importance for evaluating cardiac function and diagnosing diseases. Manual segmentation is time-consuming and tedious, so automatic segmentation is very popular in practical applications. In this paper, we propose an improved full convolutional neural network based on 2D-Unet for automatic segmentation of the left ventricle, right ventricle and myocardium. Experiments were conducted on the ACDC 2017 Challenge Training dataset. The segmentation results were assessed by means of average vertical distance, Dice coefficient and Hausdorff distance. Our model reduces the amount of parameters, improves the training speed, uses the fusion loss function, and maintains a satisfactory segmentation accuracy of left ventricle, right ventricle and myocardium.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127468257","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 : 2021-12-01DOI: 10.1109/CSCI54926.2021.00148
B. Sun, Chuan Wang, Qiang Yang, Weili Liu, Wei-jie Yu
Balanced multiple traveling salesmen problems (BMTSP) are a popular kind of combinatorial optimization problems widely existing in the real world. This problem aims to minimize the total path length of all salesmen, and at the same time minimize the longest path among all salesmen to keep the path length balance. To solve this problem effectively, this paper proposes a balance biased ant colony optimization (BACO) algorithm. Specifically, this algorithm maintains ant groups to optimize the paths of all salesmen with each ant group responsible for constructing a feasible solution and each ant in a group responsible for building the path of one salesman. To construct balanced paths for all salesmen, this paper further develops four ant selection mechanisms to construct paths, namely, Random Selection (RS), Shortest Biased Selection (SBS), Future Balance Biased Selection (FBBS) and Future Shortest Biased Selection (FSBS). Additionally, we further introduce the 2-opt local search operation to optimize the path of each salesman. Finally, extensive experiments conducted on four TSPLIB benchmark sets with different numbers of salesmen demonstrate that the proposed BACO with the four ant selection mechanisms shows much better performance than a state-of-the-art genetic algorithm (GA). In particular, among the four selection mechanisms, the FSBS strategy helps BACO achieve the best performance in solving BMSTP.
{"title":"Ant Colony Optimization for Balanced Multiple Traveling Salesmen Problem","authors":"B. Sun, Chuan Wang, Qiang Yang, Weili Liu, Wei-jie Yu","doi":"10.1109/CSCI54926.2021.00148","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00148","url":null,"abstract":"Balanced multiple traveling salesmen problems (BMTSP) are a popular kind of combinatorial optimization problems widely existing in the real world. This problem aims to minimize the total path length of all salesmen, and at the same time minimize the longest path among all salesmen to keep the path length balance. To solve this problem effectively, this paper proposes a balance biased ant colony optimization (BACO) algorithm. Specifically, this algorithm maintains ant groups to optimize the paths of all salesmen with each ant group responsible for constructing a feasible solution and each ant in a group responsible for building the path of one salesman. To construct balanced paths for all salesmen, this paper further develops four ant selection mechanisms to construct paths, namely, Random Selection (RS), Shortest Biased Selection (SBS), Future Balance Biased Selection (FBBS) and Future Shortest Biased Selection (FSBS). Additionally, we further introduce the 2-opt local search operation to optimize the path of each salesman. Finally, extensive experiments conducted on four TSPLIB benchmark sets with different numbers of salesmen demonstrate that the proposed BACO with the four ant selection mechanisms shows much better performance than a state-of-the-art genetic algorithm (GA). In particular, among the four selection mechanisms, the FSBS strategy helps BACO achieve the best performance in solving BMSTP.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130176783","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 : 2021-12-01DOI: 10.1109/CSCI54926.2021.00300
Chinari Takano, S. Fujino, Daiki Nobayashi, K. Tsukamoto, M. Mizumachi, T. Ikenaga
With the recent proliferation of Internet of Things (IoT) devices that can send and receive data via wireless communication, we are able to monitor and operate these devices remotely. An example of an IoT system using wireless communication is a system for anomaly detection in mechanical equipment using acoustic data. In order to detect anomalies using acoustic data, continuous recording is essential, thereby increasing the data size. Although Wi-Fi networks provide high-capacity data transfer, performance degradation cannot be avoided due to reasons such as packet losses caused by collisions with data from other devices using the same frequency and the increase in distance between two communicating devices. In the present study, we developed a wireless communication system for reliable acoustic data collection for anomaly detection in mechanical equipment. First, as preliminary experiments, we investigated the communication characteristics for the transmission of large-size data by Wi-Fi in indoor and outdoor environments. The results indicated the communication performance was insufficient for transferring all recorded data handled by this system. Therefore, we developed a simple heuristic transmission timing control method and a method that can reduce the amount of transmission data in order to realize a stable acoustic data collection system. Finally, through demonstration experiments using mechanical equipment in the field, we verified the feasibility of the acoustic data collection system.
{"title":"Development of a Wireless Communication System for Reliable Acoustic Data Collection Toward Anomaly Detection in Mechanical Equipment","authors":"Chinari Takano, S. Fujino, Daiki Nobayashi, K. Tsukamoto, M. Mizumachi, T. Ikenaga","doi":"10.1109/CSCI54926.2021.00300","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00300","url":null,"abstract":"With the recent proliferation of Internet of Things (IoT) devices that can send and receive data via wireless communication, we are able to monitor and operate these devices remotely. An example of an IoT system using wireless communication is a system for anomaly detection in mechanical equipment using acoustic data. In order to detect anomalies using acoustic data, continuous recording is essential, thereby increasing the data size. Although Wi-Fi networks provide high-capacity data transfer, performance degradation cannot be avoided due to reasons such as packet losses caused by collisions with data from other devices using the same frequency and the increase in distance between two communicating devices. In the present study, we developed a wireless communication system for reliable acoustic data collection for anomaly detection in mechanical equipment. First, as preliminary experiments, we investigated the communication characteristics for the transmission of large-size data by Wi-Fi in indoor and outdoor environments. The results indicated the communication performance was insufficient for transferring all recorded data handled by this system. Therefore, we developed a simple heuristic transmission timing control method and a method that can reduce the amount of transmission data in order to realize a stable acoustic data collection system. Finally, through demonstration experiments using mechanical equipment in the field, we verified the feasibility of the acoustic data collection system.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128931262","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 : 2021-12-01DOI: 10.1109/CSCI54926.2021.00081
R. Lam, C. K. Tang, K. L. Cheung, L. Lam
SARS-CoV-2 traveling through ventilating pipes in high-rise buildings present an urgent concern to address. This paper describes an IoT monitoring system designed to disinfect the air traveling through the pipes. Site tests demonstrate that the system provides a cost-effective solution for pathogen inactivation in ventilating pipes of high-rise buildings, and that it can play a positive role in mitigating the spread of the COVID-19 pandemic in built environments.
{"title":"UV-C VentGuard: An IoT-based Monitoring and Disinfection System for Pathogen Inactivation in Ventilation Pipes of High-rise Buildings","authors":"R. Lam, C. K. Tang, K. L. Cheung, L. Lam","doi":"10.1109/CSCI54926.2021.00081","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00081","url":null,"abstract":"SARS-CoV-2 traveling through ventilating pipes in high-rise buildings present an urgent concern to address. This paper describes an IoT monitoring system designed to disinfect the air traveling through the pipes. Site tests demonstrate that the system provides a cost-effective solution for pathogen inactivation in ventilating pipes of high-rise buildings, and that it can play a positive role in mitigating the spread of the COVID-19 pandemic in built environments.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132977401","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 : 2021-12-01DOI: 10.1109/CSCI54926.2021.00155
Anousha Athreya, S. K. Taswell, Andrew Kang, Ishani Das, C. Taswell
In historical artifact conservation, archiving objects using entity metadata plays a significant role in managing the related versions of the artifacts preserved, recorded and documented at various time points. In this paper, we discuss five fields of study to display the importance of related versions in identifying patterns over time through historical events, cultural heritage, brain health, performing arts, and fine arts. We describe our use of the Ashurbanipal diristry to document scholarly research on archiving tools and technologies. We highlight the importance of the provenance infosubset in tracing metadata for cultural objects managed in NPDS repositories and enabling interoperability with existing multimedia bibliographic formats including MARC and BIBFRAME.
{"title":"Ashurbanipal: A Diristry to Document Multimedia Metadata Tools for Transdisciplinary Archives","authors":"Anousha Athreya, S. K. Taswell, Andrew Kang, Ishani Das, C. Taswell","doi":"10.1109/CSCI54926.2021.00155","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00155","url":null,"abstract":"In historical artifact conservation, archiving objects using entity metadata plays a significant role in managing the related versions of the artifacts preserved, recorded and documented at various time points. In this paper, we discuss five fields of study to display the importance of related versions in identifying patterns over time through historical events, cultural heritage, brain health, performing arts, and fine arts. We describe our use of the Ashurbanipal diristry to document scholarly research on archiving tools and technologies. We highlight the importance of the provenance infosubset in tracing metadata for cultural objects managed in NPDS repositories and enabling interoperability with existing multimedia bibliographic formats including MARC and BIBFRAME.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131696519","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 : 2021-12-01DOI: 10.1109/CSCI54926.2021.00016
Michelle Hu, Yen-Hung Frank Hu
The increasing prevalence of deep learning-based machines in the daily life of average people results in a demand for research to be done on improving the accuracy of deep learning models. In response to this need, this paper aims to explore the effects of changing the parameters of a deep learning model, including the neuron count, epoch count, batch size, and validation split on the prediction accuracy of a deep learning model. We used the programming language Python, the TensorFlow and Pandas libraries, and the Keras application programming interface to create 13 regression-based deep learning models, all but one, which was used as a standard, of which had a parameter altered to be lower or higher than the standard model. After training each model using a dataset comprised of data from the 2010 United States census, we measured the predictive accuracy of each model at different epoch counts using the absolute average difference between the predictions of life expectancy from the models and the actual value from the 2010 U.S. census dataset. Based on the absolute average difference for each model, we found that increasing the neuron count, epoch count, and batch size and decreasing the validation split improves prediction accuracy in deep learning models, in most cases. These results can be used to create more accurate deep learning models for scientific or commercial use, and the models themselves can be used for their ability to predict life expectancies from given data, based on learned trends.
{"title":"The Effects of Different Parameters on the Accuracy of Deep Learning Models for Predicting U.S. Citizen’s Life Expectancy","authors":"Michelle Hu, Yen-Hung Frank Hu","doi":"10.1109/CSCI54926.2021.00016","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00016","url":null,"abstract":"The increasing prevalence of deep learning-based machines in the daily life of average people results in a demand for research to be done on improving the accuracy of deep learning models. In response to this need, this paper aims to explore the effects of changing the parameters of a deep learning model, including the neuron count, epoch count, batch size, and validation split on the prediction accuracy of a deep learning model. We used the programming language Python, the TensorFlow and Pandas libraries, and the Keras application programming interface to create 13 regression-based deep learning models, all but one, which was used as a standard, of which had a parameter altered to be lower or higher than the standard model. After training each model using a dataset comprised of data from the 2010 United States census, we measured the predictive accuracy of each model at different epoch counts using the absolute average difference between the predictions of life expectancy from the models and the actual value from the 2010 U.S. census dataset. Based on the absolute average difference for each model, we found that increasing the neuron count, epoch count, and batch size and decreasing the validation split improves prediction accuracy in deep learning models, in most cases. These results can be used to create more accurate deep learning models for scientific or commercial use, and the models themselves can be used for their ability to predict life expectancies from given data, based on learned trends.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128759320","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}
This paper presents an empirical case study on applying game-based learning in an undergraduate finance course. The paper describes the experimental study context, protocol, and results. Using multivariate regression analysis, a significant game effect on student performance is observed for competitive strategy-based games.
{"title":"Learning Finance with Games: An Empirical Study","authors":"Sandy Ingram, Rania Islambouli, Miharisoa Andrianantenaina, Jean‐Philippe Weisskopf, Philippe Masset, Nicole Baudat","doi":"10.1109/CSCI54926.2021.00205","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00205","url":null,"abstract":"This paper presents an empirical case study on applying game-based learning in an undergraduate finance course. The paper describes the experimental study context, protocol, and results. Using multivariate regression analysis, a significant game effect on student performance is observed for competitive strategy-based games.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126723607","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 : 2021-12-01DOI: 10.1109/CSCI54926.2021.00210
Md Mahmudur Rahman, Roshan Paudel Morgan
This work presents our experience and approach of teaching an introductory CS programming course remotely in this pandemic era without missing out on the benefits of hands-on learning generally used in-person learning environment. Synchronous virtual learning occurs when students join an audio/video enabled meeting space at the same time through several cloud based services, such as Zoom based conference tool, interactive cloud based coding environment in repl.it and GoSoapBox platform for online in-classroom engagement, which were well integrated in the Canvas based Learning Management System (LMS). To keep the structure of the session much like an in person learning experience, the synchronous session included whole group instruction in Zoom led by the instructor and small group (breakout room) based lab work in Repl.it amongst the learners. Both interactive and collaborative learning are infused in pedagogy effectively so that students can learn using interactive platforms, tools, technologies, systems, and services as available to them and collaborate within and among groups. To evaluate the impact of this infusion, a pre- and post-survey were conducted on student cohort (4 sections taught by 3 different instructors) in the Fall’2020 semester. In addition, final project scores and final grades for Fall’2020 semester and enrollment number and final grade distributions from Fall’2017 to Fall’2020 were also available for analysis. The initial evaluation of the survey results and student’s performances based on quality point scores show evidence to conclude that the proposed pedagogical approach increased student motivation and engagement and facilitated learning to entry-level computer science students.
{"title":"A Remote Instructional Approach with Interactive and Collaborative Learning to Teach an Introductory Programming Course during COVID-19 Pandemic","authors":"Md Mahmudur Rahman, Roshan Paudel Morgan","doi":"10.1109/CSCI54926.2021.00210","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00210","url":null,"abstract":"This work presents our experience and approach of teaching an introductory CS programming course remotely in this pandemic era without missing out on the benefits of hands-on learning generally used in-person learning environment. Synchronous virtual learning occurs when students join an audio/video enabled meeting space at the same time through several cloud based services, such as Zoom based conference tool, interactive cloud based coding environment in repl.it and GoSoapBox platform for online in-classroom engagement, which were well integrated in the Canvas based Learning Management System (LMS). To keep the structure of the session much like an in person learning experience, the synchronous session included whole group instruction in Zoom led by the instructor and small group (breakout room) based lab work in Repl.it amongst the learners. Both interactive and collaborative learning are infused in pedagogy effectively so that students can learn using interactive platforms, tools, technologies, systems, and services as available to them and collaborate within and among groups. To evaluate the impact of this infusion, a pre- and post-survey were conducted on student cohort (4 sections taught by 3 different instructors) in the Fall’2020 semester. In addition, final project scores and final grades for Fall’2020 semester and enrollment number and final grade distributions from Fall’2017 to Fall’2020 were also available for analysis. The initial evaluation of the survey results and student’s performances based on quality point scores show evidence to conclude that the proposed pedagogical approach increased student motivation and engagement and facilitated learning to entry-level computer science students.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126749732","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 : 2021-12-01DOI: 10.1109/csci54926.2021.00061
S. Samarakoon, T. Weerasinghe, Hakim Usoof
Tablet-mediated learning becomes popular among kindergarten children with the encroachment of technology . Most educational app developers target pre-school children in their app market. Therefore developers increasingly create educational apps that target this age group. The study of usability aspects of such applications is important due to the lack of cognitive and physical development of children in this age group. However, as per the literature most of the current applications for pre-school children are not successful due to a lack of consideration of usability. Hence, preschool children face many usability issues when using these applications. Due to limited cognitive and physical development, children of early primary education find it difficult to use those applications. There are many usability frameworks developed but most of them have been developed targeting adult users. So, there is no standard framework for early primary children. Hence, this research aims to propose a set of usability heuristics that can be used by the designers of tablet-mediated applications for early primary children.
{"title":"Usability Heuristics for Early Primary Children: A Case Study in Sri Lanka","authors":"S. Samarakoon, T. Weerasinghe, Hakim Usoof","doi":"10.1109/csci54926.2021.00061","DOIUrl":"https://doi.org/10.1109/csci54926.2021.00061","url":null,"abstract":"Tablet-mediated learning becomes popular among kindergarten children with the encroachment of technology . Most educational app developers target pre-school children in their app market. Therefore developers increasingly create educational apps that target this age group. The study of usability aspects of such applications is important due to the lack of cognitive and physical development of children in this age group. However, as per the literature most of the current applications for pre-school children are not successful due to a lack of consideration of usability. Hence, preschool children face many usability issues when using these applications. Due to limited cognitive and physical development, children of early primary education find it difficult to use those applications. There are many usability frameworks developed but most of them have been developed targeting adult users. So, there is no standard framework for early primary children. Hence, this research aims to propose a set of usability heuristics that can be used by the designers of tablet-mediated applications for early primary children.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123179317","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}
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