Pub Date : 2024-02-08DOI: 10.5815/ijmecs.2024.01.01
Dao Thi Thanh Loan, Nguyen Duy Tho, Nguyen Huu Nghia, Vu Dinh Chien, Tran Anh Tuan
{"title":"Analyzing Students’ Performance Using Fuzzy Logic and Hierarchical Linear Regression","authors":"Dao Thi Thanh Loan, Nguyen Duy Tho, Nguyen Huu Nghia, Vu Dinh Chien, Tran Anh Tuan","doi":"10.5815/ijmecs.2024.01.01","DOIUrl":"https://doi.org/10.5815/ijmecs.2024.01.01","url":null,"abstract":"","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139791065","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 : 2024-02-08DOI: 10.5815/ijmecs.2024.01.05
S. Porkodi, Bassam Khalil Hamdan Tabash
{"title":"A Comprehensive Meta-Analysis of Blended Learning Adoption and Technological Acceptance in Higher Education","authors":"S. Porkodi, Bassam Khalil Hamdan Tabash","doi":"10.5815/ijmecs.2024.01.05","DOIUrl":"https://doi.org/10.5815/ijmecs.2024.01.05","url":null,"abstract":"","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139793898","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 : 2024-02-08DOI: 10.5815/ijmecs.2024.01.04
Rhaydae Jebli, Jaber El Bouhdidi, M. Chkouri
{"title":"A Proposed Algorithm for Assessing and Grading Automatically Student UML Diagrams","authors":"Rhaydae Jebli, Jaber El Bouhdidi, M. Chkouri","doi":"10.5815/ijmecs.2024.01.04","DOIUrl":"https://doi.org/10.5815/ijmecs.2024.01.04","url":null,"abstract":"","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139851397","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 : 2023-12-08DOI: 10.5815/ijmecs.2023.06.03
Serhiy Balovsyak, Oleksandr Derevyanchuk, Hanna Kravchenko, Yuriy Ushenko, Zhengbing Hu
: The software for clustering students according to their educational achievements using fuzzy logic was developed in Python using the Google Colab cloud service. In the process of analyzing educational data, the problems of Data Mining are solved, since only some characteristics of the educational process are obtained from a large sample of data. Data clustering was performed using the classic K-Means method, which is characterized by simplicity and high speed. Cluster analysis was performed in the space of two features using the machine learning library scikit-learn (Python). The obtained clusters are described by fuzzy triangular membership functions, which allowed to correctly determine the membership of each student to a certain cluster. Creation of fuzzy membership functions is done using the scikit-fuzzy library. The development of fuzzy functions of objects belonging to clusters is also useful for educational purposes, as it allows a better understanding of the principles of using fuzzy logic. As a result of processing test educational data using the developed software, correct results were obtained. It is shown that the use of fuzzy membership functions makes it possible to correctly determine the belonging of students to certain clusters, even if such clusters are not clearly separated. Due to this, it is possible to more accurately determine the recommended level of difficulty of tasks for each student, depending on his previous evaluations.
{"title":"Clustering Students According to their Academic Achievement Using Fuzzy Logic","authors":"Serhiy Balovsyak, Oleksandr Derevyanchuk, Hanna Kravchenko, Yuriy Ushenko, Zhengbing Hu","doi":"10.5815/ijmecs.2023.06.03","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.06.03","url":null,"abstract":": The software for clustering students according to their educational achievements using fuzzy logic was developed in Python using the Google Colab cloud service. In the process of analyzing educational data, the problems of Data Mining are solved, since only some characteristics of the educational process are obtained from a large sample of data. Data clustering was performed using the classic K-Means method, which is characterized by simplicity and high speed. Cluster analysis was performed in the space of two features using the machine learning library scikit-learn (Python). The obtained clusters are described by fuzzy triangular membership functions, which allowed to correctly determine the membership of each student to a certain cluster. Creation of fuzzy membership functions is done using the scikit-fuzzy library. The development of fuzzy functions of objects belonging to clusters is also useful for educational purposes, as it allows a better understanding of the principles of using fuzzy logic. As a result of processing test educational data using the developed software, correct results were obtained. It is shown that the use of fuzzy membership functions makes it possible to correctly determine the belonging of students to certain clusters, even if such clusters are not clearly separated. Due to this, it is possible to more accurately determine the recommended level of difficulty of tasks for each student, depending on his previous evaluations.","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589421","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 : 2023-12-08DOI: 10.5815/ijmecs.2023.06.04
Rajgopal K T, A. S. Rao, Ramaprasad Poojary, Deepak D
{"title":"Dynamic Load Balancing in Cloud Computing: A Convergence of PSO, GSA, and Fuzzy Logic within a Hybridized Metaheuristic Framework","authors":"Rajgopal K T, A. S. Rao, Ramaprasad Poojary, Deepak D","doi":"10.5815/ijmecs.2023.06.04","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.06.04","url":null,"abstract":"","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589137","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 : 2023-12-08DOI: 10.5815/ijmecs.2023.06.02
Xuan-Quy Dao, Ngoc-Bich Le
{"title":"LLMs Performance on Vietnamese High School Biology Examination","authors":"Xuan-Quy Dao, Ngoc-Bich Le","doi":"10.5815/ijmecs.2023.06.02","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.06.02","url":null,"abstract":"","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138588833","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 : 2023-12-08DOI: 10.5815/ijmecs.2023.06.05
Reshmi P Rajan, Deepa V. Jose, Roopashree Gurumoorthy
{"title":"Tangent Search Long Short Term Memory with Aadaptive Reinforcement Transient Learning based Extractive and Abstractive Document Summarization","authors":"Reshmi P Rajan, Deepa V. Jose, Roopashree Gurumoorthy","doi":"10.5815/ijmecs.2023.06.05","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.06.05","url":null,"abstract":"","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589673","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 : 2023-12-08DOI: 10.5815/ijmecs.2023.06.06
Praveena K N, Mahalakshmi R, Manjunath C, Ahmad Faiz Zubair, P. Karthikeyan
{"title":"OptimizedFeature Selection and Transformations for Early Stage Prediction of Autism Using Supervised Machine Learning Models","authors":"Praveena K N, Mahalakshmi R, Manjunath C, Ahmad Faiz Zubair, P. Karthikeyan","doi":"10.5815/ijmecs.2023.06.06","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.06.06","url":null,"abstract":"","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589713","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}
{"title":"Predicting College Students’ Placements Based on Academic Performance Using Machine Learning Approaches","authors":"Mukesh Kumar, Nidhi Walia, Sushil Bansal, Girish Kumar, Korhan Cengiz","doi":"10.5815/ijmecs.2023.06.01","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.06.01","url":null,"abstract":"","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138588949","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 : 2023-10-08DOI: 10.5815/ijmecs.2023.05.02
Azat Tashev, Zhanar Takenova, Mukaddas Arshidinova
This article deals with the problem of optimal resources allocation in reference to the area that is currently relevant in the Republic of Kazakhstan, the educational system and management issues in educational organizations in rapidly changing economic and social conditions. A model for the optimal resources’ allocation in the management of business processes that exist in educational organizations has been developed using the example of one of the key business processes. Such research methods as search, survey, Fishbone diagrams and heuristic methods were used. A computational algorithm was developed and the testing results on the suggested example were presented. Comparative analysis shows that the developed computational algorithm based on the application of the linear programming method results in the optimal resources allocation in the considered business process. The analysis of existing methods reveals their limitations, particularly in dealing with dependent operations. The research findings and approaches have practical implications for improving the management system and enhancing the quality of business processes in educational organizations. The algorithms and models developed in this study can be applied not only to solve load distribution issues among teachers but also to address resource allocation problems in other areas of educational institutions.
{"title":"Algorithms for Solving Problems of Resources Allocation in the Management of Business Processes in Educational Organizations","authors":"Azat Tashev, Zhanar Takenova, Mukaddas Arshidinova","doi":"10.5815/ijmecs.2023.05.02","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.05.02","url":null,"abstract":"This article deals with the problem of optimal resources allocation in reference to the area that is currently relevant in the Republic of Kazakhstan, the educational system and management issues in educational organizations in rapidly changing economic and social conditions. A model for the optimal resources’ allocation in the management of business processes that exist in educational organizations has been developed using the example of one of the key business processes. Such research methods as search, survey, Fishbone diagrams and heuristic methods were used. A computational algorithm was developed and the testing results on the suggested example were presented. Comparative analysis shows that the developed computational algorithm based on the application of the linear programming method results in the optimal resources allocation in the considered business process. The analysis of existing methods reveals their limitations, particularly in dealing with dependent operations. The research findings and approaches have practical implications for improving the management system and enhancing the quality of business processes in educational organizations. The algorithms and models developed in this study can be applied not only to solve load distribution issues among teachers but also to address resource allocation problems in other areas of educational institutions.","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252452","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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