Content-free collaborative learning modeling using data mining
Abstract Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Anaya, Antonio R. [verfasserIn] Boticario, Jesús G. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: User modeling and user adapted interaction - [S.l.] : Proquest, 1991, 21(2011), 1-2 vom: 20. Jan., Seite 181-216 |
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| Übergeordnetes Werk: |
volume:21 ; year:2011 ; number:1-2 ; day:20 ; month:01 ; pages:181-216 |
| Links: |
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| DOI / URN: |
10.1007/s11257-010-9095-z |
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| Katalog-ID: |
SPR018283349 |
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| 520 | |a Abstract Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future. | ||
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| 700 | 1 | |a Boticario, Jesús G. |e verfasserin |4 aut | |
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10.1007/s11257-010-9095-z doi (DE-627)SPR018283349 (SPR)s11257-010-9095-z-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Anaya, Antonio R. verfasserin aut Content-free collaborative learning modeling using data mining 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future. Collaborative learning (dpeaa)DE-He213 Collaboration modeling (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Open models (dpeaa)DE-He213 Collaboration evaluation (dpeaa)DE-He213 Meta-cognitive tools in collaborative learning (dpeaa)DE-He213 Boticario, Jesús G. verfasserin aut Enthalten in User modeling and user adapted interaction [S.l.] : Proquest, 1991 21(2011), 1-2 vom: 20. Jan., Seite 181-216 (DE-627)269758828 (DE-600)1475734-5 1573-1391 nnns volume:21 year:2011 number:1-2 day:20 month:01 pages:181-216 https://dx.doi.org/10.1007/s11257-010-9095-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 ASE AR 21 2011 1-2 20 01 181-216 |
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10.1007/s11257-010-9095-z doi (DE-627)SPR018283349 (SPR)s11257-010-9095-z-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Anaya, Antonio R. verfasserin aut Content-free collaborative learning modeling using data mining 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future. Collaborative learning (dpeaa)DE-He213 Collaboration modeling (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Open models (dpeaa)DE-He213 Collaboration evaluation (dpeaa)DE-He213 Meta-cognitive tools in collaborative learning (dpeaa)DE-He213 Boticario, Jesús G. verfasserin aut Enthalten in User modeling and user adapted interaction [S.l.] : Proquest, 1991 21(2011), 1-2 vom: 20. Jan., Seite 181-216 (DE-627)269758828 (DE-600)1475734-5 1573-1391 nnns volume:21 year:2011 number:1-2 day:20 month:01 pages:181-216 https://dx.doi.org/10.1007/s11257-010-9095-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 ASE AR 21 2011 1-2 20 01 181-216 |
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10.1007/s11257-010-9095-z doi (DE-627)SPR018283349 (SPR)s11257-010-9095-z-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Anaya, Antonio R. verfasserin aut Content-free collaborative learning modeling using data mining 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future. Collaborative learning (dpeaa)DE-He213 Collaboration modeling (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Open models (dpeaa)DE-He213 Collaboration evaluation (dpeaa)DE-He213 Meta-cognitive tools in collaborative learning (dpeaa)DE-He213 Boticario, Jesús G. verfasserin aut Enthalten in User modeling and user adapted interaction [S.l.] : Proquest, 1991 21(2011), 1-2 vom: 20. Jan., Seite 181-216 (DE-627)269758828 (DE-600)1475734-5 1573-1391 nnns volume:21 year:2011 number:1-2 day:20 month:01 pages:181-216 https://dx.doi.org/10.1007/s11257-010-9095-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 ASE AR 21 2011 1-2 20 01 181-216 |
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10.1007/s11257-010-9095-z doi (DE-627)SPR018283349 (SPR)s11257-010-9095-z-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Anaya, Antonio R. verfasserin aut Content-free collaborative learning modeling using data mining 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future. Collaborative learning (dpeaa)DE-He213 Collaboration modeling (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Open models (dpeaa)DE-He213 Collaboration evaluation (dpeaa)DE-He213 Meta-cognitive tools in collaborative learning (dpeaa)DE-He213 Boticario, Jesús G. verfasserin aut Enthalten in User modeling and user adapted interaction [S.l.] : Proquest, 1991 21(2011), 1-2 vom: 20. Jan., Seite 181-216 (DE-627)269758828 (DE-600)1475734-5 1573-1391 nnns volume:21 year:2011 number:1-2 day:20 month:01 pages:181-216 https://dx.doi.org/10.1007/s11257-010-9095-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 ASE AR 21 2011 1-2 20 01 181-216 |
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10.1007/s11257-010-9095-z doi (DE-627)SPR018283349 (SPR)s11257-010-9095-z-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Anaya, Antonio R. verfasserin aut Content-free collaborative learning modeling using data mining 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future. Collaborative learning (dpeaa)DE-He213 Collaboration modeling (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Open models (dpeaa)DE-He213 Collaboration evaluation (dpeaa)DE-He213 Meta-cognitive tools in collaborative learning (dpeaa)DE-He213 Boticario, Jesús G. verfasserin aut Enthalten in User modeling and user adapted interaction [S.l.] : Proquest, 1991 21(2011), 1-2 vom: 20. Jan., Seite 181-216 (DE-627)269758828 (DE-600)1475734-5 1573-1391 nnns volume:21 year:2011 number:1-2 day:20 month:01 pages:181-216 https://dx.doi.org/10.1007/s11257-010-9095-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 ASE AR 21 2011 1-2 20 01 181-216 |
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Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. 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Anaya, Antonio R. |
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Anaya, Antonio R. ddc 004 bkl 54.00 misc Collaborative learning misc Collaboration modeling misc Data mining misc Open models misc Collaboration evaluation misc Meta-cognitive tools in collaborative learning Content-free collaborative learning modeling using data mining |
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ddc 004 bkl 54.00 misc Collaborative learning misc Collaboration modeling misc Data mining misc Open models misc Collaboration evaluation misc Meta-cognitive tools in collaborative learning |
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Content-free collaborative learning modeling using data mining |
| abstract |
Abstract Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future. |
| abstractGer |
Abstract Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future. |
| abstract_unstemmed |
Abstract Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future. |
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| container_issue |
1-2 |
| title_short |
Content-free collaborative learning modeling using data mining |
| url |
https://dx.doi.org/10.1007/s11257-010-9095-z |
| remote_bool |
true |
| author2 |
Boticario, Jesús G. |
| author2Str |
Boticario, Jesús G. |
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| doi_str |
10.1007/s11257-010-9095-z |
| up_date |
2024-07-03T18:41:16.368Z |
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| score |
7.3992968 |