MoodleREC: A recommendation system for creating courses using the moodle e-learning platform
The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In par...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
De Medio, Carlo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: (Neo-)segregation, (neo-)racism, and one-city two-system planning in Windhoek, Namibia: What can a new national urban policy do? - Kohima, Jennilee Magdalena ELSEVIER, 2022, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:104 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.chb.2019.106168 |
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| Katalog-ID: |
ELV049479229 |
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| 520 | |a The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. | ||
| 520 | |a The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. | ||
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10.1016/j.chb.2019.106168 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000921.pica (DE-627)ELV049479229 (ELSEVIER)S0747-5632(19)30380-2 DE-627 ger DE-627 rakwb eng 630 640 320 VZ 48.00 bkl De Medio, Carlo verfasserin aut MoodleREC: A recommendation system for creating courses using the moodle e-learning platform 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. E-learning Elsevier Learning object Elsevier Recommender systems Elsevier Learning object repository Elsevier Limongelli, Carla oth Sciarrone, Filippo oth Temperini, Marco oth Enthalten in Elsevier Science Kohima, Jennilee Magdalena ELSEVIER (Neo-)segregation, (neo-)racism, and one-city two-system planning in Windhoek, Namibia: What can a new national urban policy do? 2022 Amsterdam [u.a.] (DE-627)ELV008973938 volume:104 year:2020 pages:0 https://doi.org/10.1016/j.chb.2019.106168 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 104 2020 0 |
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10.1016/j.chb.2019.106168 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000921.pica (DE-627)ELV049479229 (ELSEVIER)S0747-5632(19)30380-2 DE-627 ger DE-627 rakwb eng 630 640 320 VZ 48.00 bkl De Medio, Carlo verfasserin aut MoodleREC: A recommendation system for creating courses using the moodle e-learning platform 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. E-learning Elsevier Learning object Elsevier Recommender systems Elsevier Learning object repository Elsevier Limongelli, Carla oth Sciarrone, Filippo oth Temperini, Marco oth Enthalten in Elsevier Science Kohima, Jennilee Magdalena ELSEVIER (Neo-)segregation, (neo-)racism, and one-city two-system planning in Windhoek, Namibia: What can a new national urban policy do? 2022 Amsterdam [u.a.] (DE-627)ELV008973938 volume:104 year:2020 pages:0 https://doi.org/10.1016/j.chb.2019.106168 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 104 2020 0 |
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10.1016/j.chb.2019.106168 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000921.pica (DE-627)ELV049479229 (ELSEVIER)S0747-5632(19)30380-2 DE-627 ger DE-627 rakwb eng 630 640 320 VZ 48.00 bkl De Medio, Carlo verfasserin aut MoodleREC: A recommendation system for creating courses using the moodle e-learning platform 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. E-learning Elsevier Learning object Elsevier Recommender systems Elsevier Learning object repository Elsevier Limongelli, Carla oth Sciarrone, Filippo oth Temperini, Marco oth Enthalten in Elsevier Science Kohima, Jennilee Magdalena ELSEVIER (Neo-)segregation, (neo-)racism, and one-city two-system planning in Windhoek, Namibia: What can a new national urban policy do? 2022 Amsterdam [u.a.] (DE-627)ELV008973938 volume:104 year:2020 pages:0 https://doi.org/10.1016/j.chb.2019.106168 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 104 2020 0 |
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10.1016/j.chb.2019.106168 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000921.pica (DE-627)ELV049479229 (ELSEVIER)S0747-5632(19)30380-2 DE-627 ger DE-627 rakwb eng 630 640 320 VZ 48.00 bkl De Medio, Carlo verfasserin aut MoodleREC: A recommendation system for creating courses using the moodle e-learning platform 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. E-learning Elsevier Learning object Elsevier Recommender systems Elsevier Learning object repository Elsevier Limongelli, Carla oth Sciarrone, Filippo oth Temperini, Marco oth Enthalten in Elsevier Science Kohima, Jennilee Magdalena ELSEVIER (Neo-)segregation, (neo-)racism, and one-city two-system planning in Windhoek, Namibia: What can a new national urban policy do? 2022 Amsterdam [u.a.] (DE-627)ELV008973938 volume:104 year:2020 pages:0 https://doi.org/10.1016/j.chb.2019.106168 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 104 2020 0 |
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10.1016/j.chb.2019.106168 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000921.pica (DE-627)ELV049479229 (ELSEVIER)S0747-5632(19)30380-2 DE-627 ger DE-627 rakwb eng 630 640 320 VZ 48.00 bkl De Medio, Carlo verfasserin aut MoodleREC: A recommendation system for creating courses using the moodle e-learning platform 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. E-learning Elsevier Learning object Elsevier Recommender systems Elsevier Learning object repository Elsevier Limongelli, Carla oth Sciarrone, Filippo oth Temperini, Marco oth Enthalten in Elsevier Science Kohima, Jennilee Magdalena ELSEVIER (Neo-)segregation, (neo-)racism, and one-city two-system planning in Windhoek, Namibia: What can a new national urban policy do? 2022 Amsterdam [u.a.] (DE-627)ELV008973938 volume:104 year:2020 pages:0 https://doi.org/10.1016/j.chb.2019.106168 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 104 2020 0 |
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The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. |
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The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. |
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