Publicação
Advances in artificial intelligence to model student-centred VLEs
| Resumo: | The challenges to the educational paradigm, introduced with the change from a teacher to a student centred paradigm, has several implications in the organization of all the educational system. The adoption of e-learning in all of the educational levels, was a huge step forward in terms of access to resources without constrains of space and time. But e-learning didn’t reach the plato in lifelong learning scenarios, because we have a great diversity of learning contexts in universities, with more students come back to update their knowledge when they are already in the work market. The paradigm “one size fits all” is still being applied and all the pedagogical approaches adopt the traditional lecturer centred paradigm. Student centred learning is an educational paradigm that gives students greater autonomy and control over choice of subject matter, learning methods and pace of study (Gibbs, 1992). This approach is very similar to the Bologna Process goals in terms of student centred model based on learning outcomes and competences. Some of the characteristics of effective learners in the student centred learning paradigm are (de la Harpe et al., 1999): • Have clear learning goals; • Have a wide repertoire of learning strategies and know when to use them; • Use available resources effectively; • Know about their strengths and weaknesses; • Understand the learning process; • Deal appropriately with their feelings; • Take responsibility for their own learning; • Plan, monitor, evaluate and adapt their learning process. The majority of virtual learning environments (VLE) are used as mere repositories of content, based on the classroom paradigm and don’t support the individualization of the learning process. According to Dias (Dias, 2004), building spaces for online learning is a challenge that goes beyond the simple transfer of content to the Web. This approach tends to transform the environments in online repositories of information rather than in the desired spaces of interaction and experimentation. To allow a greater adaptation of the learning environment based on the student's profile, is proposed the adoption of theories of artificial intelligence in education, based on the learning experience, adapting contents and contexts to the student needs. |
|---|---|
| Autores principais: | Alves, Paulo |
| Assunto: | Learning styles Case-based reasoning Adaptive web-based educational systems |
| Ano: | 2010 |
| País: | Portugal |
| Tipo de documento: | capítulo de livro |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867173343891816448 |
|---|---|
| author | Alves, Paulo |
| author_facet | Alves, Paulo |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Alves, Paulo\",\"Person.identifier.orcid\":\"0000-0002-0100-8691\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Alves, Paulo |
| datacite.date.Accepted.fl_str_mv | 2010-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2011-05-23T15:20:54Z |
| datacite.date.embargoed.fl_str_mv | 2011-05-23T15:20:54Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Learning styles Case-based reasoning Adaptive web-based educational systems |
| datacite.titles.title.fl_str_mv | Advances in artificial intelligence to model student-centred VLEs |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Alves, Paulo |
| dc.date.Accepted.fl_str_mv | 2010-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2011-05-23T15:20:54Z |
| dc.date.embargoed.fl_str_mv | 2011-05-23T15:20:54Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/4475 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | INTECH |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Learning styles Case-based reasoning Adaptive web-based educational systems |
| dc.title.fl_str_mv | Advances in artificial intelligence to model student-centred VLEs |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_3248 |
| description | The challenges to the educational paradigm, introduced with the change from a teacher to a student centred paradigm, has several implications in the organization of all the educational system. The adoption of e-learning in all of the educational levels, was a huge step forward in terms of access to resources without constrains of space and time. But e-learning didn’t reach the plato in lifelong learning scenarios, because we have a great diversity of learning contexts in universities, with more students come back to update their knowledge when they are already in the work market. The paradigm “one size fits all” is still being applied and all the pedagogical approaches adopt the traditional lecturer centred paradigm. Student centred learning is an educational paradigm that gives students greater autonomy and control over choice of subject matter, learning methods and pace of study (Gibbs, 1992). This approach is very similar to the Bologna Process goals in terms of student centred model based on learning outcomes and competences. Some of the characteristics of effective learners in the student centred learning paradigm are (de la Harpe et al., 1999): • Have clear learning goals; • Have a wide repertoire of learning strategies and know when to use them; • Use available resources effectively; • Know about their strengths and weaknesses; • Understand the learning process; • Deal appropriately with their feelings; • Take responsibility for their own learning; • Plan, monitor, evaluate and adapt their learning process. The majority of virtual learning environments (VLE) are used as mere repositories of content, based on the classroom paradigm and don’t support the individualization of the learning process. According to Dias (Dias, 2004), building spaces for online learning is a challenge that goes beyond the simple transfer of content to the Web. This approach tends to transform the environments in online repositories of information rather than in the desired spaces of interaction and experimentation. To allow a greater adaptation of the learning environment based on the student's profile, is proposed the adoption of theories of artificial intelligence in education, based on the learning experience, adapting contents and contexts to the student needs. |
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| person_str_mv | Alves, Paulo Alves, Paulo https://www.ciencia-id.pt/C319-FC42-5B6B C319-FC42-5B6B http://orcid.org/0000-0002-0100-8691 0000-0002-0100-8691 |
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| spelling | engINTECHporThe challenges to the educational paradigm, introduced with the change from a teacher to a student centred paradigm, has several implications in the organization of all the educational system. The adoption of e-learning in all of the educational levels, was a huge step forward in terms of access to resources without constrains of space and time. But e-learning didn’t reach the plato in lifelong learning scenarios, because we have a great diversity of learning contexts in universities, with more students come back to update their knowledge when they are already in the work market. The paradigm “one size fits all” is still being applied and all the pedagogical approaches adopt the traditional lecturer centred paradigm. Student centred learning is an educational paradigm that gives students greater autonomy and control over choice of subject matter, learning methods and pace of study (Gibbs, 1992). This approach is very similar to the Bologna Process goals in terms of student centred model based on learning outcomes and competences. Some of the characteristics of effective learners in the student centred learning paradigm are (de la Harpe et al., 1999): • Have clear learning goals; • Have a wide repertoire of learning strategies and know when to use them; • Use available resources effectively; • Know about their strengths and weaknesses; • Understand the learning process; • Deal appropriately with their feelings; • Take responsibility for their own learning; • Plan, monitor, evaluate and adapt their learning process. The majority of virtual learning environments (VLE) are used as mere repositories of content, based on the classroom paradigm and don’t support the individualization of the learning process. According to Dias (Dias, 2004), building spaces for online learning is a challenge that goes beyond the simple transfer of content to the Web. This approach tends to transform the environments in online repositories of information rather than in the desired spaces of interaction and experimentation. To allow a greater adaptation of the learning environment based on the student's profile, is proposed the adoption of theories of artificial intelligence in education, based on the learning experience, adapting contents and contexts to the student needs.application/pdfporAdvances in artificial intelligence to model student-centred VLEsPersonalAlves, PauloDSpacehttp://dspace.org/items/43d3b0cd-8fd9-4194-a9df-9cca66f8726bDSpacehttp://dspace.org/items/43d3b0cd-8fd9-4194-a9df-9cca66f8726bAlvesPauloCiência IDhttps://www.ciencia-id.ptC319-FC42-5B6BORCIDhttp://orcid.org0000-0002-0100-8691Scopus Author IDhttps://www.scopus.com55834442100HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-953-7619-56-52011-05-23T15:20:54Z20102010-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/4475http://purl.org/coar/access_right/c_abf2open accessLearning stylesCase-based reasoningAdaptive web-based educational systems178351 bytesliteraturehttp://purl.org/coar/resource_type/c_3248book parthttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/e0646b00-7198-432e-b745-da789a24adbb/downloadAdvances in Learning Processes113124 |
| spellingShingle | Advances in artificial intelligence to model student-centred VLEs Alves, Paulo Learning styles Case-based reasoning Adaptive web-based educational systems |
| status | SINGLETON |
| subject.fl_str_mv | Learning styles Case-based reasoning Adaptive web-based educational systems |
| title | Advances in artificial intelligence to model student-centred VLEs |
| title_full | Advances in artificial intelligence to model student-centred VLEs |
| title_fullStr | Advances in artificial intelligence to model student-centred VLEs |
| title_full_unstemmed | Advances in artificial intelligence to model student-centred VLEs |
| title_short | Advances in artificial intelligence to model student-centred VLEs |
| title_sort | Advances in artificial intelligence to model student-centred VLEs |
| topic | Learning styles Case-based reasoning Adaptive web-based educational systems |
| topic_facet | Learning styles Case-based reasoning Adaptive web-based educational systems |
| url | http://hdl.handle.net/10198/4475 |
| visible | 1 |