Data Mining and Computational Intelligence for E-learning and Education
This Reprint, Data Mining and Computational Intelligence for E-learning and Education, presents a collection of cutting-edge research focused on the application of artificial intelligence to educational contexts. The selected contributions explore how data mining and intelligent algorithms can be us...
Đã lưu trong:
| Định dạng: | Online |
|---|---|
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
|
| Những chủ đề: | |
| Truy cập trực tuyến: | ONIX_20250812T110751_9783725840304_298 |
| Các nhãn: |
Không có thẻ, Là người đầu tiên thẻ bản ghi này!
|
| _version_ | 1869523890951159808 |
|---|---|
| collection | Directory of Open Access Books |
| description | This Reprint, Data Mining and Computational Intelligence for E-learning and Education, presents a collection of cutting-edge research focused on the application of artificial intelligence to educational contexts. The selected contributions explore how data mining and intelligent algorithms can be used to analyze learning behaviors, predict academic outcomes, personalize educational experiences, and optimize decision-making processes. Spanning both traditional and digital learning environments, the works featured here address real-world problems and demonstrate practical AI-based solutions, including the use of adaptive systems, chatbots, and predictive models. This Reprint also considers ethical concerns associated with the use of AI in education, offering a well-rounded view of the challenges and opportunities in this evolving field. This volume serves as a valuable reference for researchers, educators, and developers seeking to understand and harness the transformative power of computational intelligence in education. |
| format | Online |
| id | doab-20.500.12854ir-165543 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1655432025-08-12T09:53:39Z Data Mining and Computational Intelligence for E-learning and Education Cabezuelo, Antonio Sarasa González del Campo Rodríguez Barbero, Ramón data mining tools WEKA J48 algorithm KAPPA value predict confusion matrix csv school students COVID-19 mental health social support picture fuzzy force field analysis (PF-FFA) level based weight assessment (LBWA) COVID omicron online learning remote learning online education Twitter dataset tweets social media big data dropout prediction student attrition machine learning educational data mining learning analytics educational innovation higher education action recognition cheating computer vision feature extraction video surveillance academic performance machine learning in education imbalanced classes multi-class classification learning management system prediction thematic analysis Indonesia physics education research clustering data mining DBSCAN K-Means HDBSCAN entrepreneurial intentions measurement invariance multigroup analysis gender Zimbabwe federated learning Learning Management System Technology Acceptance Model Cumulative Link Mixed Model descriptive network analysis student dropout classification data sampling imbalanced datasets digital literacy dataset IC3 certification improvement RMUTT thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries This Reprint, Data Mining and Computational Intelligence for E-learning and Education, presents a collection of cutting-edge research focused on the application of artificial intelligence to educational contexts. The selected contributions explore how data mining and intelligent algorithms can be used to analyze learning behaviors, predict academic outcomes, personalize educational experiences, and optimize decision-making processes. Spanning both traditional and digital learning environments, the works featured here address real-world problems and demonstrate practical AI-based solutions, including the use of adaptive systems, chatbots, and predictive models. This Reprint also considers ethical concerns associated with the use of AI in education, offering a well-rounded view of the challenges and opportunities in this evolving field. This volume serves as a valuable reference for researchers, educators, and developers seeking to understand and harness the transformative power of computational intelligence in education. 2025-08-12T09:53:36Z 2025-08-12T09:53:36Z 2025 book ONIX_20250812T110751_9783725840304_298 9783725840304 9783725840298 https://directory.doabooks.org/handle/20.500.12854/165543 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10871 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4029-8 10.3390/books978-3-7258-4029-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725840304 9783725840298 258 open access |
| spellingShingle | data mining tools WEKA J48 algorithm KAPPA value predict confusion matrix csv school students COVID-19 mental health social support picture fuzzy force field analysis (PF-FFA) level based weight assessment (LBWA) COVID omicron online learning remote learning online education dataset tweets social media big data dropout prediction student attrition machine learning educational data mining learning analytics educational innovation higher education action recognition cheating computer vision feature extraction video surveillance academic performance machine learning in education imbalanced classes multi-class classification learning management system prediction thematic analysis Indonesia physics education research clustering data mining DBSCAN K-Means HDBSCAN entrepreneurial intentions measurement invariance multigroup analysis gender Zimbabwe federated learning Learning Management System Technology Acceptance Model Cumulative Link Mixed Model descriptive network analysis student dropout classification data sampling imbalanced datasets digital literacy dataset IC3 certification improvement RMUTT thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Data Mining and Computational Intelligence for E-learning and Education |
| title | Data Mining and Computational Intelligence for E-learning and Education |
| title_full | Data Mining and Computational Intelligence for E-learning and Education |
| title_fullStr | Data Mining and Computational Intelligence for E-learning and Education |
| title_full_unstemmed | Data Mining and Computational Intelligence for E-learning and Education |
| title_short | Data Mining and Computational Intelligence for E-learning and Education |
| title_sort | data mining and computational intelligence for e learning and education |
| topic | data mining tools WEKA J48 algorithm KAPPA value predict confusion matrix csv school students COVID-19 mental health social support picture fuzzy force field analysis (PF-FFA) level based weight assessment (LBWA) COVID omicron online learning remote learning online education dataset tweets social media big data dropout prediction student attrition machine learning educational data mining learning analytics educational innovation higher education action recognition cheating computer vision feature extraction video surveillance academic performance machine learning in education imbalanced classes multi-class classification learning management system prediction thematic analysis Indonesia physics education research clustering data mining DBSCAN K-Means HDBSCAN entrepreneurial intentions measurement invariance multigroup analysis gender Zimbabwe federated learning Learning Management System Technology Acceptance Model Cumulative Link Mixed Model descriptive network analysis student dropout classification data sampling imbalanced datasets digital literacy dataset IC3 certification improvement RMUTT thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | data mining tools WEKA J48 algorithm KAPPA value predict confusion matrix csv school students COVID-19 mental health social support picture fuzzy force field analysis (PF-FFA) level based weight assessment (LBWA) COVID omicron online learning remote learning online education dataset tweets social media big data dropout prediction student attrition machine learning educational data mining learning analytics educational innovation higher education action recognition cheating computer vision feature extraction video surveillance academic performance machine learning in education imbalanced classes multi-class classification learning management system prediction thematic analysis Indonesia physics education research clustering data mining DBSCAN K-Means HDBSCAN entrepreneurial intentions measurement invariance multigroup analysis gender Zimbabwe federated learning Learning Management System Technology Acceptance Model Cumulative Link Mixed Model descriptive network analysis student dropout classification data sampling imbalanced datasets digital literacy dataset IC3 certification improvement RMUTT thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20250812T110751_9783725840304_298 |