LEADER 04213nam  22006495  450 
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010   $a9783319034195
010   $a3319034197
024 7 $a10.1007/978-3-319-03419-5
035   $a(CKB)3710000000078613
035   $a(DE-He213)978-3-319-03419-5
035   $a(SSID)ssj0001066945
035   $a(PQKBManifestationID)11630046
035   $a(PQKBTitleCode)TC0001066945
035   $a(PQKBWorkID)11072782
035   $a(PQKB)10249284
035   $a(MiAaPQ)EBC3091984
035   $a(PPN)17610786X
035   $a(EXLCZ)993710000000078613
100   $a20131125d2014      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAnalyzing discourse and text complexity for learning and collaborating $ea cognitive approach based on natural language processing  /$fMihai Dasc?lu
210  1$aCham :$cSpringer,$d2014.
215   $a1 online resource (xiv, 279 pages) $cillustrations (some color)
225 1 $aStudies in cmputational intelligence,$x1860-949X ;$vvolume 534
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$a9783319034188 
311 08$a3319034189 
320   $aIncludes bibliographical references.
327   $aIndividual Learning -- Collaborative Learning -- Overview of Empirical Studies -- Dialogism.
330   $aWith the advent and increasing popularity of Computer Supported Collaborative Learning (CSCL) and e-learning technologies, the need of automatic assessment and of teacher/tutor support for the two tightly intertwined activities of comprehension of reading materials and of collaboration among peers has grown significantly. In this context, a polyphonic model of discourse derived from Bakhtin?s work as a paradigm is used for analyzing both general texts and CSCL conversations in a unique framework focused on different facets of textual cohesion. As specificity of our analysis, the individual learning perspective is focused on the identification of reading strategies and on providing a multi-dimensional textual complexity model, whereas the collaborative learning dimension is centered on the evaluation of participants? involvement, as well as on collaboration assessment. Our approach based on advanced Natural Language Processing techniques provides a qualitative estimation of the learning process and enhances understanding as a ?mediator of learning? by providing automated feedback to both learners and teachers or tutors. The main benefits are its flexibility, extensibility and nevertheless specificity for covering multiple stages, starting from reading classroom materials, to discussing on specific topics in a collaborative manner, and finishing the feedback loop by verbalizing metacognitive thoughts.
410  0$aStudies in computational intelligence :$vv. 534.$x1860-949X
606   $aComputer-assisted instruction
606   $aNatural language processing (Computer science)
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040
606   $aLearning & Instruction$3https://scigraph.springernature.com/ontologies/product-market-codes/O22000
606   $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022
606   $aPhilosophy of Language$3https://scigraph.springernature.com/ontologies/product-market-codes/E26000
615  0$aComputer-assisted instruction.
615  0$aNatural language processing (Computer science)
615 14$aComputational Intelligence.
615 24$aNatural Language Processing (NLP).
615 24$aLearning & Instruction.
615 24$aComplexity.
615 24$aPhilosophy of Language.
676   $a006.3
700   $aDasc?lu$b Mihai$4aut$4http://id.loc.gov/vocabulary/relators/aut$0856509
906   $aBOOK
912   $a9910299487103321
996   $aAnalyzing Discourse and Text Complexity for Learning and Collaborating$92143767
997   $aUNINA