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008 160915s2014 xxu| s |||| 0|eng d
020 _a9781493905300
_9978-1-4939-0530-0
024 7 _a10.1007/978-1-4939-0530-0
_2doi
035 _ato000541507
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aRecommender Systems for Technology Enhanced Learning
_helectronic resource
_bResearch Trends and Applications /
_cedited by Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Olga C. Santos.
260 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aXIV, 306 p. 67 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aCollaborative Filtering Recommendation of Educational Content in Social Environments utilizing Sentiment Analysis Techniques -- Towards automated evaluation of learning resources inside repositories -- Linked Data and the Social Web as facilitators for TEL recommender systems in research and practice -- The Learning Registry: Applying Social Metadata for Learning Resource Recommendations -- A Framework for Personalised Learning-Plan Recommendations in Game-Based Learning -- An approach for an Affective Educational Recommendation Model -- The Case for Preference-Inconsistent Recommendations -- Further Thoughts on Context-Aware Paper Recommendations for Education -- Towards a Social Trust-aware Recommender for Teachers -- ALEF: from Application to Platform for Adaptive Collaborative Learning -- Two Recommending Strategies to enhance Online Presence in Personal Learning Environments -- Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem -- COCOON CORE: CO-Author Recommendations based on Betweenness Centrality and Interest Similarity -- Scientific Recommendations to Enhance Scholarly Awareness and Foster Collaboration.
520 _aAs an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years. Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices. Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.
650 0 _aComputer Science.
_9155490
650 0 _aInformation systems.
_9303226
650 0 _aArtificial intelligence.
_9274099
650 1 4 _aComputer Science.
_9155490
650 2 4 _aArtificial Intelligence (incl. Robotics).
_9274102
650 2 4 _aEducation (general).
_9303863
650 2 4 _aInformation Systems and Communication Service.
_9304271
700 1 _aManouselis, Nikos.
_eeditor.
_9445563
700 1 _aDrachsler, Hendrik.
_eeditor.
_9445564
700 1 _aVerbert, Katrien.
_eeditor.
_9445565
700 1 _aSantos, Olga C.
_eeditor.
_9445566
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4939-0530-0
912 _aZDB-2-SCS
999 _c398548