LEADER 04409nam 2200577 450 001 9910465251603321 005 20210422212228.0 010 $a3-486-78127-8 024 7 $a10.1524/9783486781274 035 $a(CKB)2560000000312982 035 $a(EBL)1394788 035 $a(MiAaPQ)EBC1394788 035 $a(DE-B1597)217274 035 $a(OCoLC)881296517 035 $a(OCoLC)922475027 035 $a(DE-B1597)9783486781274 035 $a(Au-PeEL)EBL1394788 035 $a(CaPaEBR)ebr11074529 035 $a(CaONFJC)MIL805859 035 $a(EXLCZ)992560000000312982 100 $a20140624h20132013 uy| 0 101 0 $aeng 135 $aur|nu---|u||u 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 14$aThe inverted classroom model $ethe 2nd German ICM-Conference - proceedings /$fProf. Dr. Ju?rgen Handke, Natalie Kiesler, Leonie Wiemeyer 210 1$aMu?nchen :$cOldenbourg Verlag,$d[2013] 210 4$d©2013 215 $a1 online resource (201 p.) 300 $aDescription based upon print version of record. 311 $a3-486-80251-8 311 $a3-486-74185-3 320 $aIncludes bibliographical references and index. 327 $tFront matter --$tContents --$tPreface --$tThe Authors --$tI Recent Developments in ICM Implementation --$t1 The Inverted Classroom: Where to Go from Here /$rLoviscach, Jörn --$t2 Beyond a Simple ICM /$rHandke, Jürgen --$t3 Activating Students by Inverting and Shuffling the Classroom - Experiences from Employing ICM and I²CM /$rMöller, Clemens --$t4 Experiences with the Implementation of an Inverted Classroom Course to Promote Key Competences /$rVassiliou, Athanasios --$tII Phase 1 of the Inverted Classroom Model: Content Delivery --$t5 Learning by Contribution - Using Wikis in Higher Education /$rBleimann, Udo / Löw, Robert --$t6 The VLC Video Strategy /$rHandke, Jürgen --$t7 Using Videos in the Linguistics Classroom /$rKiesler, Natalie --$t8 Flipping Professional Training in Higher Education Didactics - Proposing an Open Video Platform /$rTacke, Oliver --$tIII Phase 2 of the Inverted Classroom Model: In-Class Activities --$t9 Tutor of the Day - A New Didactic Concept for the Practice Phase of ICM-Based Teaching /$rGünther, Anne --$t10 Designing In-Class Activities in the Inverted Classroom Model /$rSpannagel, Christian / Spannagel, Janna --$t11 Clicker-Happy: Audience Response Systems as an Interface between Pre-Class Preparation and In-Class Session --$tIV Implementation of the ICM in High School --$t12 Flipped Learning in the Science Classroom /$rBennett, Brian E. --$t13 Inverting the History Classroom - A First-Hand Report /$rBernsen, Daniel --$t14 Inverting a Competence-Based EFL Classroom - A Model for Advanced Learner Activation? /$rWeidmann, Dirk --$tReferences --$tIndex 330 $aWhen the 1st German Inverted Classroom Conference was staged in 2012, the organizers thought that it may have been the first and last conference of this kind: Too few teachers seemed to be familiar with this model in the first place and only a tiny fragment of them would actually apply this model to their own teaching scenarios. However, in the 2013 conference, we were overwhelmed with a large number of teachers who not only wanted to find out about this teaching and learning concept but had already used it. Consequently, the focus of the 2nd German Inverted Classroom Conference to which this conference volume is dedicated was no longer the "installation" of the Inverted Classroom Model (ICM) but fine adjustments in the actual application of it. This is reflected in the contributions to this volume. Even though all three central aspects of the ICM are addressed, (1) content production and delivery, (2) testing, and (3) the in-class phase, there has been a shift away from mere content production towards an expansion of the model as well as a move towards fine adjustments of the three components. 606 $aActive learning$vCongresses 608 $aElectronic books. 615 0$aActive learning 676 $a371.39 686 $aDP 1960$2rvk 702 $aHandke$b Ju?rgen 702 $aKiesler$b Natalie 702 $aWiemeyer$b Leonie 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910465251603321 996 $aThe inverted classroom model$92461520 997 $aUNINA LEADER 01580nam a2200361 i 4500 001 991000727329707536 005 20020507172815.0 008 930916s1987 it 000 0 ita d 020 $a8808062945 035 $ab1074888x-39ule_inst 035 $aLE01301360$9ExL 040 $aDip.to Matematica$beng 082 0 $a519.2$222 084 $aAMS 60-01 084 $aAMS 60-XX 100 1 $aDall'Aglio, Giorgio$07501 245 10$aCalcolo delle probabilità /$cGiorgio Dall'Aglio 260 $aBologna :$bZanichelli,$c1987 300 $aviii, 300 p. ;$c25 cm 490 0 $aCollana di matematica. Testi e manuali 650 0$aProbability calculus 650 0$aProbability theory 650 0$aStochastic processes 907 $a.b1074888x$b23-02-17$c28-06-02 912 $a991000727329707536 945 $aLE003 519 DAL01.01 C.1 (1987)$g1$i2003000020977$lle003$o-$pE0.00$q-$rn$s- $t0$u2$v0$w2$x0$y.i12057587$z18-12-02 945 $aLE003 519 DAL01.01 C.2 (1987)$g2$i2003000020984$lle003$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i12057599$z18-12-02 945 $aLE013 60-XX DAL11 C.1 (1987)$g1$i2013000070476$lle013$o-$pE0.00$q-$rl$s- $t0$u13$v11$w13$x0$y.i10841489$z28-06-02 945 $aLE013 60-XX DAL11 C.2 (1987)$g2$i2013000149974$lle013$o-$pE0.00$q-$rl$s- $t0$u12$v12$w12$x0$y.i10841490$z28-06-02 945 $aLE025 ECO 519 CAL01.01$g1$i2025000018129$lle025$o-$pE0.00$q-$rl$s- $t0$u5$v8$w5$x0$y.i13939166$z16-11-04 996 $aCalcolo delle probabilità$973486 997 $aUNISALENTO 998 $ale003$ale013$ale025$b01-01-93$cm$da $e-$fita$git $h0$i2 LEADER 03989nam 22007575 450 001 9910299960303321 005 20200702232627.0 010 $a3-319-70609-8 024 7 $a10.1007/978-3-319-70609-2 035 $a(CKB)4340000000223546 035 $a(DE-He213)978-3-319-70609-2 035 $a(MiAaPQ)EBC6298184 035 $a(MiAaPQ)EBC5590649 035 $a(Au-PeEL)EBL5590649 035 $a(OCoLC)1066192745 035 $a(PPN)221251995 035 $a(EXLCZ)994340000000223546 100 $a20171104d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFrom Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces /$fby Jacek Grekow 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XIV, 138 p. 71 illus., 22 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v747 311 $a3-319-70608-X 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Representations of Emotions -- Human Annotation -- MIDI Features -- Hierarchical Emotion Detection in MIDI Files. 330 $aThe problems it addresses include emotion representation, annotation of music excerpts, feature extraction, and machine learning. The book chiefly focuses on content-based analysis of music files, a system that automatically analyzes the structures of a music file and annotates the file with the perceived emotions. Further, it explores emotion detection in MIDI and audio files. In the experiments presented here, the categorical and dimensional approaches were used, and the knowledge and expertise of music experts with a university music education were used for music file annotation. The automatic emotion detection systems constructed and described in the book make it possible to index and subsequently search through music databases according to emotion. In turn, the emotion maps of musical compositions provide valuable new insights into the distribution of emotions in music and can be used to compare that distribution in different compositions, or to conduct emotional comparisons of different interpretations of the same composition. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v747 606 $aComputational intelligence 606 $aMusic 606 $aAcoustical engineering 606 $aEmotions 606 $aPattern perception 606 $aAcoustics 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aMusic$3https://scigraph.springernature.com/ontologies/product-market-codes/417000 606 $aEngineering Acoustics$3https://scigraph.springernature.com/ontologies/product-market-codes/T16000 606 $aEmotion$3https://scigraph.springernature.com/ontologies/product-market-codes/Y20140 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aAcoustics$3https://scigraph.springernature.com/ontologies/product-market-codes/P21069 615 0$aComputational intelligence. 615 0$aMusic. 615 0$aAcoustical engineering. 615 0$aEmotions. 615 0$aPattern perception. 615 0$aAcoustics. 615 14$aComputational Intelligence. 615 24$aMusic. 615 24$aEngineering Acoustics. 615 24$aEmotion. 615 24$aPattern Recognition. 615 24$aAcoustics. 676 $a780.285 700 $aGrekow$b Jacek$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063953 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299960303321 996 $aFrom Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces$92535461 997 $aUNINA