@prefix ocrer: <http://purl.org/net/OCRe/research.owl#> .
@prefix owl:   <http://www.w3.org/2002/07/owl#> .
@prefix scires: <http://vivoweb.org/ontology/scientific-research#> .
@prefix xsd:   <http://www.w3.org/2001/XMLSchema#> .
@prefix skos:  <http://www.w3.org/2004/02/skos/core#> .
@prefix rdfs:  <http://www.w3.org/2000/01/rdf-schema#> .
@prefix ocresd: <http://purl.org/net/OCRe/study_design.owl#> .
@prefix swo:   <http://www.ebi.ac.uk/efo/swo/> .
@prefix cito:  <http://purl.org/spar/cito/> .
@prefix geo:   <http://aims.fao.org/aos/geopolitical.owl#> .
@prefix ocresst: <http://purl.org/net/OCRe/statistics.owl#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix vivo:  <http://vivoweb.org/ontology/core#> .
@prefix event: <http://purl.org/NET/c4dm/event.owl#> .
@prefix vann:  <http://purl.org/vocab/vann/> .
@prefix foaf:  <http://xmlns.com/foaf/0.1/> .
@prefix c4o:   <http://purl.org/spar/c4o/> .
@prefix fabio: <http://purl.org/spar/fabio/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix thkoeln: <http://cris.nrw/hisinone#> .
@prefix vitro: <http://vitro.mannlib.cornell.edu/ns/vitro/0.7#> .
@prefix vitro-public: <http://vitro.mannlib.cornell.edu/ns/vitro/public#> .
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix ocresp: <http://purl.org/net/OCRe/study_protocol.owl#> .
@prefix bibo:  <http://purl.org/ontology/bibo/> .
@prefix obo:   <http://purl.obolibrary.org/obo/> .
@prefix ro:    <http://purl.obolibrary.org/obo/ro.owl#> .

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        rdfs:label              "RDF description of Novices Make More Noise! But How Can We Listen to it? - https://fis.th-koeln.de/vivo/individual/publ_13377" , "Novices Make More Noise! But How Can We Listen to it?" ;
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        bibo:abstract           "This paper presents an extension to the approach described in [13] which was designed to help distinguish expert and novice performance easily by observing the sensor data without having to understand nor apply models to the sensor signal. The method consisted of plotting the sensor data and identifying irregularities in novice data and regularities in expert data. In this paper, we solidify the thesis that, with the help of sensors, expert performances are smoother, contain fewer irregularities, and have consistently uniform patterns than novice performances. We do so using the extended methodology on the same data set from the previous five cases in [13], namely running, bachata dance, salsa dance, tennis swings, and football penalty kicks, pointing out this assertion. © 2023 Copyright for this paper by its authors." ;
        bibo:isbn13             "#MODS-ID.bibthk_mods_00011370" , "#defaultkonferenzband" ;
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        bibo:pageStart          "56" ;
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        vivo:dateTimeValue      <http://cris.nrw/date2023> ;
        vivo:freetextKeyword    "Multimodal Learning Analytics" , "Equity" , "Sensors" , "and Inclusion" , "Signal Interpretation" , "Diversity" ;
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