Editorial: educational big data: extracting meaning from data for smart education
Main Author: | |
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Publication Date: | 2020 |
Other Authors: | , , |
Format: | Article |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | http://hdl.handle.net/10400.2/16412 |
Summary: | Data are a valuable resource in education, with a panoply of applications across all educational levels, subjects and stakeholders. When data becomes overwhelming in terms of volume and complexity, innovative techniques are required to extract meaning and transform this data into valuable information. This special issue of the journal focuses on the value of Educational Big Data in the context of different education levels, from pre-school to higher education. The manuscripts included in this issue approach the potential of EBD for academic performance prediction, learning analytics implementation, and the identification and improvement of student behavioral patterns and performance. They are centred on students and emphasize the role that technology can play in improving their learning experience and performance. |
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Editorial: educational big data: extracting meaning from data for smart educationEducational big dataDataAcademic performance predictionData are a valuable resource in education, with a panoply of applications across all educational levels, subjects and stakeholders. When data becomes overwhelming in terms of volume and complexity, innovative techniques are required to extract meaning and transform this data into valuable information. This special issue of the journal focuses on the value of Educational Big Data in the context of different education levels, from pre-school to higher education. The manuscripts included in this issue approach the potential of EBD for academic performance prediction, learning analytics implementation, and the identification and improvement of student behavioral patterns and performance. They are centred on students and emphasize the role that technology can play in improving their learning experience and performance.Taylor and Francis Ltd.Repositório AbertoChen, Nian-shingYin, ChengjiuIsaias, PedroPsotka, Joseph2024-08-06T16:12:36Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.2/16412eng1049-482010.1080/10494820.2019.1635395info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-26T09:45:21Zoai:repositorioaberto.uab.pt:10400.2/16412Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T21:07:18.858413Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
Editorial: educational big data: extracting meaning from data for smart education |
title |
Editorial: educational big data: extracting meaning from data for smart education |
spellingShingle |
Editorial: educational big data: extracting meaning from data for smart education Chen, Nian-shing Educational big data Data Academic performance prediction |
title_short |
Editorial: educational big data: extracting meaning from data for smart education |
title_full |
Editorial: educational big data: extracting meaning from data for smart education |
title_fullStr |
Editorial: educational big data: extracting meaning from data for smart education |
title_full_unstemmed |
Editorial: educational big data: extracting meaning from data for smart education |
title_sort |
Editorial: educational big data: extracting meaning from data for smart education |
author |
Chen, Nian-shing |
author_facet |
Chen, Nian-shing Yin, Chengjiu Isaias, Pedro Psotka, Joseph |
author_role |
author |
author2 |
Yin, Chengjiu Isaias, Pedro Psotka, Joseph |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório Aberto |
dc.contributor.author.fl_str_mv |
Chen, Nian-shing Yin, Chengjiu Isaias, Pedro Psotka, Joseph |
dc.subject.por.fl_str_mv |
Educational big data Data Academic performance prediction |
topic |
Educational big data Data Academic performance prediction |
description |
Data are a valuable resource in education, with a panoply of applications across all educational levels, subjects and stakeholders. When data becomes overwhelming in terms of volume and complexity, innovative techniques are required to extract meaning and transform this data into valuable information. This special issue of the journal focuses on the value of Educational Big Data in the context of different education levels, from pre-school to higher education. The manuscripts included in this issue approach the potential of EBD for academic performance prediction, learning analytics implementation, and the identification and improvement of student behavioral patterns and performance. They are centred on students and emphasize the role that technology can play in improving their learning experience and performance. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 2020-01-01T00:00:00Z 2024-08-06T16:12:36Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.2/16412 |
url |
http://hdl.handle.net/10400.2/16412 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1049-4820 10.1080/10494820.2019.1635395 |
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info:eu-repo/semantics/openAccess |
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openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Taylor and Francis Ltd. |
publisher.none.fl_str_mv |
Taylor and Francis Ltd. |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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