000			03980nam a22005295i 4500
001			978-1-4939-0530-0
003			DE-He213
005			20200421111156.0
007			cr nn 008mamaa
008			140412s2014 xxu| s |||| 0|eng d
020			_a9781493905300 _9978-1-4939-0530-0
024	7		_a10.1007/978-1-4939-0530-0 _2doi
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 _h[electronic resource] : _bResearch Trends and Applications / _cedited by Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Olga C. Santos.
264		1	_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
347			_atext file _bPDF _2rda
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.
650		0	_aComputers.
650		0	_aArtificial intelligence.
650		0	_aEducation.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aEducation, general.
650	2	4	_aInformation Systems and Communication Service.
700	1		_aManouselis, Nikos. _eeditor.
700	1		_aDrachsler, Hendrik. _eeditor.
700	1		_aVerbert, Katrien. _eeditor.
700	1		_aSantos, Olga C. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781493905294
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4939-0530-0
912			_aZDB-2-SCS
942			_cEBK
999			_c53546 _d53546