Pub Date : 2023-05-23DOI: 10.1109/SERA57763.2023.10197814
Anna Sheila I. Crisostomo, R. Encarnacion, Shakir Al Balushi
Higher educational institutions’ goal is to ensure that graduates are imbibed with the required employability skills. Hence, by applying the concept of Knowledge Discover in Database (KDD), this study aims to build a graduates’ employment prediction model using classification task in Bayes and Tree Methods. The data utilized for this purpose are collected from the tracer survey conducted to Oman Tourism College alumni. Based on the graduates’ profiles, the generated model predicts whether a graduate is employed full-time, part-time, self-employed or unemployed. Using several classification techniques provided by Waikato Environment for Knowledge Analysis (WEKA), the findings revealed that RandomTree algorithm and REPTree algorithms, under decision tree methods yielded accuracy rates of 96.3636% and 88.1818% respectively. BayesNet algorithm, a variant of Bayes algorithm yielded an accuracy of 84.5455%, ranked third. Information gain and ranker method are also used for attribute ranking which showed occupation as the most influential factor for employability followed by job sector. Other attributes used in classifying the employment status of the graduates include occupation, job sector, specialization, degree, age, personality development skills, cultural competency, leadership, interpersonal skills, creativity, and problem-solving skills. This experiment concludes that a tree-based classifier is the most suitable algorithm for predicting tourism graduates’ employability in the tourism and hospitality sector of the Sultanate of Oman.
高等教育机构的目标是确保毕业生具备所需的就业技能。因此，本研究运用KDD (Knowledge Discover in Database)的概念，利用贝叶斯分类任务和树方法构建毕业生就业预测模型。用于此目的的数据是从对阿曼旅游学院校友进行的示踪剂调查中收集的。根据毕业生的个人资料，生成的模型预测毕业生是全职、兼职、自雇还是失业。使用Waikato Environment for Knowledge Analysis (WEKA)提供的几种分类技术，研究结果表明，在决策树方法下，RandomTree算法和REPTree算法的准确率分别为96.3636%和88.1818%。BayesNet算法是Bayes算法的一种变体，准确率为84.5455%，排名第三。利用信息增益法和排名法进行属性排序，结果显示职业是影响就业能力的最主要因素，其次是工作部门。用于分类毕业生就业状况的其他属性包括职业、工作部门、专业、学位、年龄、个性发展技能、文化能力、领导能力、人际交往能力、创造力和解决问题的能力。本实验得出结论，基于树的分类器是预测阿曼苏丹国旅游和酒店业旅游毕业生就业能力的最合适算法。
{"title":"A Data Mining Approach to Construct Classification Model for Predicting Tourism Graduates Employability","authors":"Anna Sheila I. Crisostomo, R. Encarnacion, Shakir Al Balushi","doi":"10.1109/SERA57763.2023.10197814","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197814","url":null,"abstract":"Higher educational institutions’ goal is to ensure that graduates are imbibed with the required employability skills. Hence, by applying the concept of Knowledge Discover in Database (KDD), this study aims to build a graduates’ employment prediction model using classification task in Bayes and Tree Methods. The data utilized for this purpose are collected from the tracer survey conducted to Oman Tourism College alumni. Based on the graduates’ profiles, the generated model predicts whether a graduate is employed full-time, part-time, self-employed or unemployed. Using several classification techniques provided by Waikato Environment for Knowledge Analysis (WEKA), the findings revealed that RandomTree algorithm and REPTree algorithms, under decision tree methods yielded accuracy rates of 96.3636% and 88.1818% respectively. BayesNet algorithm, a variant of Bayes algorithm yielded an accuracy of 84.5455%, ranked third. Information gain and ranker method are also used for attribute ranking which showed occupation as the most influential factor for employability followed by job sector. Other attributes used in classifying the employment status of the graduates include occupation, job sector, specialization, degree, age, personality development skills, cultural competency, leadership, interpersonal skills, creativity, and problem-solving skills. This experiment concludes that a tree-based classifier is the most suitable algorithm for predicting tourism graduates’ employability in the tourism and hospitality sector of the Sultanate of Oman.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120992371","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}
Traditional Chinese medicine (TCM) has a unique advantage of preventive treatment of diseases, and adopting the concept of early intervention can effectively prevent diseases. Using knowledge graph is an effective way while the knowledge in the field of TCM is huge and messy. However, the structure of the TCM knowledge graph is often relatively sparse, which makes it highly limited. To this end, a rule-based compositional representation learning (RCRL) model is proposed. RCRL uses the implicit rules in the TCM knowledge graph, which solves the problem of poor representation learning due to the sparse structure of the TCM knowledge graph to a certain extent. Extensive experiments are conducted on the TCM knowledge graph and public datasets, and they are compared with other baselines. Experimental results show that RCRL is superior to other baselines, with improved learning accuracy and interpretability, and can be used for various downstream tasks.
{"title":"Rule-Based Representation Learning for Traditional Chinese Medicine Knowledge Graph","authors":"Dongsheng Shi, Feng Lin, Yuxun Li, Qianzhong Chen, Y. Lin, Wentao Zhu, Dongmei Li, Xiaoping Zhang","doi":"10.1109/SERA57763.2023.10197724","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197724","url":null,"abstract":"Traditional Chinese medicine (TCM) has a unique advantage of preventive treatment of diseases, and adopting the concept of early intervention can effectively prevent diseases. Using knowledge graph is an effective way while the knowledge in the field of TCM is huge and messy. However, the structure of the TCM knowledge graph is often relatively sparse, which makes it highly limited. To this end, a rule-based compositional representation learning (RCRL) model is proposed. RCRL uses the implicit rules in the TCM knowledge graph, which solves the problem of poor representation learning due to the sparse structure of the TCM knowledge graph to a certain extent. Extensive experiments are conducted on the TCM knowledge graph and public datasets, and they are compared with other baselines. Experimental results show that RCRL is superior to other baselines, with improved learning accuracy and interpretability, and can be used for various downstream tasks.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127274082","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197792
Xianshan Qu, N. Huynh, R. Mullen, J. Rose
Each Department of Transportation in the United States must provide to the Federal Highway Administration on annual basis the number and types of vehicles traveled on its state-maintained roads. These data are fed into the Highway Performance Monitoring System used to assess the nation’s highway system performance. Classifying vehicles (i.e., identifying their types, e.g., passenger cars, trucks, etc.) during nighttime is quite challenging due to limited lighting. This study designed and evaluated three Convolutional Neural Network (CNN) models to classify vehicles using their thermal images. These three models have architectures that differ in the number of layers and, in the case of the third model, the addition of an inception layer. Of these, the second model achieves the best performance, achieving mean accuracy scores of greater than 97% for each of the three vehicle classes and f1 scores of greater than 98%. We proposed two training-test methods based on data augmentation to avoid over-fitting and to improve performance. The experimental results demonstrated that a data augmentation training-test method improves model performance further with regard to both accuracy and f1-score.
{"title":"Nighttime Vehicle Classification based on Thermal Images","authors":"Xianshan Qu, N. Huynh, R. Mullen, J. Rose","doi":"10.1109/SERA57763.2023.10197792","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197792","url":null,"abstract":"Each Department of Transportation in the United States must provide to the Federal Highway Administration on annual basis the number and types of vehicles traveled on its state-maintained roads. These data are fed into the Highway Performance Monitoring System used to assess the nation’s highway system performance. Classifying vehicles (i.e., identifying their types, e.g., passenger cars, trucks, etc.) during nighttime is quite challenging due to limited lighting. This study designed and evaluated three Convolutional Neural Network (CNN) models to classify vehicles using their thermal images. These three models have architectures that differ in the number of layers and, in the case of the third model, the addition of an inception layer. Of these, the second model achieves the best performance, achieving mean accuracy scores of greater than 97% for each of the three vehicle classes and f1 scores of greater than 98%. We proposed two training-test methods based on data augmentation to avoid over-fitting and to improve performance. The experimental results demonstrated that a data augmentation training-test method improves model performance further with regard to both accuracy and f1-score.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128226250","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197706
S. E. V. S. Pillai, Wen-Chen Hu
As of April 2023, over 6 million people have lost their lives due to COVID-19 according to the World Health Organization (WHO). With no prior knowledge of this disease, people have turned to the Internet including social media to search for available remedies. However, it is important to note that the Internet cannot replace primary healthcare providers as there is a significant amount of false information. This research proposes a system to identify fake news by combining the results from several ensemble learning methods (including bagging, boosting, stacking, & voting means) and recurrent neural network (RNN). Additionally, sentiment and emotional analyses are employed to determine whether the accuracy of fake news detection can be improved. Experiment results show the ensemble learning methods provide higher accuracy than standalone RNN model. Moreover, this study reveals that incorporating sentiment and emotional analyses in fake news detection improves the accuracy of misinformation identification.
{"title":"Misinformation Detection Using an Ensemble Method with Emphasis on Sentiment and Emotional Analyses","authors":"S. E. V. S. Pillai, Wen-Chen Hu","doi":"10.1109/SERA57763.2023.10197706","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197706","url":null,"abstract":"As of April 2023, over 6 million people have lost their lives due to COVID-19 according to the World Health Organization (WHO). With no prior knowledge of this disease, people have turned to the Internet including social media to search for available remedies. However, it is important to note that the Internet cannot replace primary healthcare providers as there is a significant amount of false information. This research proposes a system to identify fake news by combining the results from several ensemble learning methods (including bagging, boosting, stacking, & voting means) and recurrent neural network (RNN). Additionally, sentiment and emotional analyses are employed to determine whether the accuracy of fake news detection can be improved. Experiment results show the ensemble learning methods provide higher accuracy than standalone RNN model. Moreover, this study reveals that incorporating sentiment and emotional analyses in fake news detection improves the accuracy of misinformation identification.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124705227","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197797
Marwa Chnib, Wafa Gabsi
Big data systems are stable enough to store and process large volumes of quickly changing data. However, these systems are composed of massive hardware resources, which can easily cause their subcomponents to fail. Fault tolerance is a key attribute of such systems as they maintain availability, reliability and constant performance during failures. Implementing efficient fault-tolerant solutions in big data presents a challenge because fault tolerance has to satisfy some constraints related to system performance and resource consumption. To protect online computer systems from malicious attacks or malfunctions, log anomaly detection is crucial. This paper provides a new approach to identify anomalous log sequences in the HDFS (Hadoop Distributed File System) log dataset using three algorithms: Logbert, DeepLog and LOF. Then, it assess performance of all algorithms in terms of accuracy, recall, and F1-score.
{"title":"Detection of anomalies in the HDFS dataset","authors":"Marwa Chnib, Wafa Gabsi","doi":"10.1109/SERA57763.2023.10197797","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197797","url":null,"abstract":"Big data systems are stable enough to store and process large volumes of quickly changing data. However, these systems are composed of massive hardware resources, which can easily cause their subcomponents to fail. Fault tolerance is a key attribute of such systems as they maintain availability, reliability and constant performance during failures. Implementing efficient fault-tolerant solutions in big data presents a challenge because fault tolerance has to satisfy some constraints related to system performance and resource consumption. To protect online computer systems from malicious attacks or malfunctions, log anomaly detection is crucial. This paper provides a new approach to identify anomalous log sequences in the HDFS (Hadoop Distributed File System) log dataset using three algorithms: Logbert, DeepLog and LOF. Then, it assess performance of all algorithms in terms of accuracy, recall, and F1-score.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133987951","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197696
Jinbao Song, Jiahui Cai, Ran Li, Yanan Li
The characteristics of modern information dissemination are fast, convenient, timeliness and extensive. If you want to spread the scientific research achievement transformation system to more enterprise users more widely, the less operations and input of enterprise users, the better. Therefore, in the design of the scientific research achievement transformation system, the preliminary design uses less operations and shorter time to let users clearly understand the scientific research achievement transformation system. For this purpose, the scientific research achievement transformation cloud exhibition hall and the scientific research achievement transformation platform are designed to realize the dual functions of the wide dissemination of the system and the transaction of achievements. This paper firstly introduces the Taro framework and FastAPI framework of the development system, designs the functional modules of the system from user requirements, designs the relevant databases according to the attributes of existing scientific research results, and designs functional modules for enterprise users, administrators and teachers respectively. Secondly, it introduces the realization of the specific functions of the transformation system of scientific research achievements, and finally the subject is summarized.
{"title":"Design and Implementation of Scientific Research Achievement Transformation System","authors":"Jinbao Song, Jiahui Cai, Ran Li, Yanan Li","doi":"10.1109/SERA57763.2023.10197696","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197696","url":null,"abstract":"The characteristics of modern information dissemination are fast, convenient, timeliness and extensive. If you want to spread the scientific research achievement transformation system to more enterprise users more widely, the less operations and input of enterprise users, the better. Therefore, in the design of the scientific research achievement transformation system, the preliminary design uses less operations and shorter time to let users clearly understand the scientific research achievement transformation system. For this purpose, the scientific research achievement transformation cloud exhibition hall and the scientific research achievement transformation platform are designed to realize the dual functions of the wide dissemination of the system and the transaction of achievements. This paper firstly introduces the Taro framework and FastAPI framework of the development system, designs the functional modules of the system from user requirements, designs the relevant databases according to the attributes of existing scientific research results, and designs functional modules for enterprise users, administrators and teachers respectively. Secondly, it introduces the realization of the specific functions of the transformation system of scientific research achievements, and finally the subject is summarized.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133361645","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197769
Zhicheng Xu, Weinan Gao, Zhicun Chen, Rami J. Haddad, Scot Hudson, Ezebuugo Nwaonumah, Frank Zahiri, Jeremy Johnson
In this paper, a data-driven framework was designed to predict manufacturing failure. The framework includes an autoregression model with the least mean square algorithm, a linear regression model with prediction intervals for short-term and long-term failure detection, and a feature extraction model with empirical mode decomposition. The analytical results validate that the designed data-driven model is a good candidate for failure predictions in smart manufacturing processes.
{"title":"Data-Driven Smart Manufacturing Technologies for Prop Shop Systems","authors":"Zhicheng Xu, Weinan Gao, Zhicun Chen, Rami J. Haddad, Scot Hudson, Ezebuugo Nwaonumah, Frank Zahiri, Jeremy Johnson","doi":"10.1109/SERA57763.2023.10197769","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197769","url":null,"abstract":"In this paper, a data-driven framework was designed to predict manufacturing failure. The framework includes an autoregression model with the least mean square algorithm, a linear regression model with prediction intervals for short-term and long-term failure detection, and a feature extraction model with empirical mode decomposition. The analytical results validate that the designed data-driven model is a good candidate for failure predictions in smart manufacturing processes.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115431275","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197817
H. Alabsi, Majed Almotairi, Yahya Alqahtani, M. Alyami
Digital badges are an effective avenue for students to obtain recognition for their achievements. Current digital badging platforms are mainly developed to assist earners in sharing their achievements on social media platforms. These systems focus less on in-demand skills required by the labor market. However, our proposed system introduces a criteria-based badging system, requiring layers of evaluations and verifications to assure originality and quality in the process of earning a single badge. As a result, students can engage further in courses that will benefit them in terms of job readiness and preparedness. This leads students to earn badges associated with top in-demand skills required by the labor market. In turn, this can increase students’ opportunities to obtain jobs related to their skills. These badges can influence recruiting decisions because employers may find and display their required candidate qualifications and skills via recruiting channels.
{"title":"Enhancing Students’ Job Seeking Process Through A Digital Badging System","authors":"H. Alabsi, Majed Almotairi, Yahya Alqahtani, M. Alyami","doi":"10.1109/SERA57763.2023.10197817","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197817","url":null,"abstract":"Digital badges are an effective avenue for students to obtain recognition for their achievements. Current digital badging platforms are mainly developed to assist earners in sharing their achievements on social media platforms. These systems focus less on in-demand skills required by the labor market. However, our proposed system introduces a criteria-based badging system, requiring layers of evaluations and verifications to assure originality and quality in the process of earning a single badge. As a result, students can engage further in courses that will benefit them in terms of job readiness and preparedness. This leads students to earn badges associated with top in-demand skills required by the labor market. In turn, this can increase students’ opportunities to obtain jobs related to their skills. These badges can influence recruiting decisions because employers may find and display their required candidate qualifications and skills via recruiting channels.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114342145","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197690
Nicia Guillén Yparrea, Felipe Hernández-Rodríguez
This article explores the potential of the metaverse as a tool to reinforce collaborative work and improve student participation. A Rally was conducted as a playful activity in the metaverse, and data were collected through a validated survey to assess the impact on student motivation, participation, and collaboration. The main idea was to take advantage of the features of the metaverse to create an interactive and motivating learning environment that fosters collaboration and the exchange of ideas among students. To achieve this, interdisciplinary teams were formed that allowed students to interact and work together in problem solving and knowledge construction. The results of the research show that the use of playful activities in the metaverse improves student participation and motivation, as well as their ability to collaborate and communicate effectively. In conclusion, the proposal presented in this article offers an innovative and effective alternative for the reinforcement of collaborative work in the context of online education and collaboration.
{"title":"Reinforcement of Collaborative Work in the Metaverse Through Playful Activities","authors":"Nicia Guillén Yparrea, Felipe Hernández-Rodríguez","doi":"10.1109/SERA57763.2023.10197690","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197690","url":null,"abstract":"This article explores the potential of the metaverse as a tool to reinforce collaborative work and improve student participation. A Rally was conducted as a playful activity in the metaverse, and data were collected through a validated survey to assess the impact on student motivation, participation, and collaboration. The main idea was to take advantage of the features of the metaverse to create an interactive and motivating learning environment that fosters collaboration and the exchange of ideas among students. To achieve this, interdisciplinary teams were formed that allowed students to interact and work together in problem solving and knowledge construction. The results of the research show that the use of playful activities in the metaverse improves student participation and motivation, as well as their ability to collaborate and communicate effectively. In conclusion, the proposal presented in this article offers an innovative and effective alternative for the reinforcement of collaborative work in the context of online education and collaboration.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117296410","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 : 2023-05-23DOI: 10.1109/SERA57763.2023.10197810
Haotian Yan, T. Goto, Tadaaki Kirishima, K. Tsuchida
Information and communication technologies have spread rapidly, and as a result, people's attention to computer skills has reached a level as never before. However, most conventional skill assessment tools are often multiple-choice types and ask only for PC knowledge but not PC skills. Meanwhile, skill assessment using eye tracking has already been realized in the medical field and has been proven to be a reliable tool for skill assessment and may apply to other fields. Therefore, unlike the knowledge-questioning type PC skill assessment, this study is to propose a method to measure an operator's real skill level using PC operation log data. In this study, we use the operator's PC operation logs to generate heatmaps and operation features and compare these data with standard data generated by the KML model in order to assess the operator's pc skills.
{"title":"Improve Accuracy of PC Skill Assessment Using PC Operation Log Data","authors":"Haotian Yan, T. Goto, Tadaaki Kirishima, K. Tsuchida","doi":"10.1109/SERA57763.2023.10197810","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197810","url":null,"abstract":"Information and communication technologies have spread rapidly, and as a result, people's attention to computer skills has reached a level as never before. However, most conventional skill assessment tools are often multiple-choice types and ask only for PC knowledge but not PC skills. Meanwhile, skill assessment using eye tracking has already been realized in the medical field and has been proven to be a reliable tool for skill assessment and may apply to other fields. Therefore, unlike the knowledge-questioning type PC skill assessment, this study is to propose a method to measure an operator's real skill level using PC operation log data. In this study, we use the operator's PC operation logs to generate heatmaps and operation features and compare these data with standard data generated by the KML model in order to assess the operator's pc skills.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"432 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120880175","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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