{"data":{"id":"10.6084/m9.figshare.6194573","type":"dois","attributes":{"doi":"10.6084/m9.figshare.6194573","prefix":"10.6084","suffix":"m9.figshare.6194573","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Palomo-Duarte, Manuel","nameType":"Personal","givenName":"Manuel","familyName":"Palomo-Duarte","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6002-0319","nameIdentifierScheme":"ORCID"}]},{"name":"Berns, Anke","nameType":"Personal","givenName":"Anke","familyName":"Berns","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Hidden Room game data clustering in University of Cadiz (Spain) by DeutschUCA"}],"publisher":"figshare","container":{},"publicationYear":2018,"subjects":[{"subject":"130309 Learning Sciences","subjectScheme":"FOR"},{"subject":"FOS: Educational sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Educational sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"200307 German Language","subjectScheme":"FOR"},{"subject":"FOS: Languages and literature","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Languages and literature","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80109 Pattern Recognition and Data Mining","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2018-04-27","dateType":"Created"},{"date":"2018-04-30","dateType":"Updated"},{"date":"2018","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["775506 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Histograms and results of k-means and Ward's clustering for Hidden Room game (OpenSimulator) in University of Cadiz (Spain) by DeutschUCA\u003cbr\u003e\u003cbr\u003eThe fileset contains information from three sources:\u003cbr\u003e\u003cbr\u003e1. Histograms files:\u003cbr\u003e* Lexical_histogram.png (histogram of lexical error ratios)\u003cbr\u003e* Grammatical_histogram.png (histogram of grammatical error ratios)\u003cbr\u003e\u003cbr\u003e2. K-means clustering files:\u003cbr\u003e* elbow-lex kmeans.png (clustering by lexical aspects: error curves obtained for applying elbow method to determinate the optimal number of clusters)\u003cbr\u003e* cube-lex kmeans.png (clustering by lexical aspects: a three-dimensional representation of clusters obtained after applying k-means method)\u003cbr\u003e* Lexical_clusters (table) kmeans.xls (clustering by lexical aspects: centroids, standard deviations and number of instances assigned to each cluster)\u003cbr\u003e* elbow-gram kmeans.png (clustering by grammatical aspects: error curves obtained for applying elbow method to determinate the optimal number of clusters)\u003cbr\u003e* cube-gramm kmeans.png (clustering by grammatical aspects: a three-dimensional representation of clusters obtained after applying k-means method)\u003cbr\u003e* Grammatical_clusters (table) kmeans.xls (clustering by grammatical aspects: centroids, standard deviations and number of instances assigned to each cluster)\u003cbr\u003e* elbow-lexgram kmeans.png (clustering by lexical and grammatical aspects: error curves obtained for applying elbow method to determinate the optimal number of clusters)\u003cbr\u003e* Lexical_Grammatical_clusters (table) kmeans.xls (clustering by lexical and grammatical aspects: centroids, standard deviations and number of instances assigned to each cluster)\u003cbr\u003e* Grammatical_clusters_number_of_words (table) kmeans.xls : number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying k-means clustering to grammatical error ratios.\u003cbr\u003e* Lexical_clusters_number_of_words (table) kmeans.xls : number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying k-means clustering to lexical error ratios.\u003cbr\u003e* Lexical_Grammatical_clusters_number_of_words (table) kmeans.xls : number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying k-means clustering to lexical and grammatical error ratios.\u003cbr\u003e\u003cbr\u003e3. Ward’s Agglomerative Hierarchical Clustering files:\u003cbr\u003e* Lexical_Cluster_Dendrogram_ward.png (clustering by lexical aspects: dendrogram obtained after applying Ward's clustering method).\u003cbr\u003e* Grammatical_Cluster_Dendrogram_ward.png (clustering by grammatical aspects: dendrogram obtained after applying Ward's clustering method)\u003cbr\u003e* Lexical_Grammatical_Cluster_Dendrogram_ward.png (clustering by lexical and grammatical aspects: dendrogram obtained after applying Ward's clustering method)\u003cbr\u003e* Lexical_Grammatical_clusters (table) ward.xls: Centroids (from column 2 to 7) and cluster sizes (last column) obtained by applying Ward's agglomerative hierarchical clustering to lexical and grammatical error ratios.\u003cbr\u003e* Grammatical_clusters (table) ward.xls: Centroids (from column 2 to 4) and cluster sizes (last column) obtained by applying Ward's agglomerative hierarchical clustering to grammatical error ratios.\u003cbr\u003e* Lexical_clusters (table) ward.xls: Centroids (from column 2 to 4) and cluster sizes (last column) obtained by applying Ward's agglomerative hierarchical clustering to lexical error ratios.\u003cbr\u003e* Lexical_clusters_number_of_words (table) ward.xls: number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying Ward's agglomerative hierarchical clustering to lexical error ratios.\u003cbr\u003e* Grammatical_clusters_number_of_words (table) ward.xls: number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying Ward's agglomerative hierarchical clustering to grammatical error ratios.\u003cbr\u003e* Lexical_Grammatical_clusters_number_of_words (table) ward.xls: number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying Ward's agglomerative hierarchical clustering to lexical and grammatical error ratios.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.6084/M9.FIGSHARE.6194573</identifier>
  <creators>
    <creator>
      <creatorName>Manuel Palomo-duarte</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org">0000-0001-6002-0319</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Anke Berns</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Hidden Room game data clustering in University of Cadiz (Spain) by DeutschUCA</title>
  </titles>
  <descriptions>
    <description descriptionType="Abstract">Histograms and results of k-means and Ward's clustering for Hidden Room game (OpenSimulator) in University of Cadiz (Spain) by DeutschUCA&lt;br&gt;&lt;br&gt;The fileset contains information from three sources:&lt;br&gt;&lt;br&gt;1. Histograms files:&lt;br&gt;* Lexical_histogram.png (histogram of lexical error ratios)&lt;br&gt;* Grammatical_histogram.png (histogram of grammatical error ratios)&lt;br&gt;&lt;br&gt;2. K-means clustering files:&lt;br&gt;*
 elbow-lex kmeans.png (clustering by lexical aspects: error curves 
obtained for applying elbow method to determinate the optimal number of 
clusters)&lt;br&gt;* cube-lex kmeans.png (clustering by lexical aspects: a 
three-dimensional representation of  clusters obtained after applying 
k-means method)&lt;br&gt;* Lexical_clusters (table) kmeans.xls (clustering by 
lexical aspects: centroids, standard deviations and number of instances 
assigned to each cluster)&lt;br&gt;* elbow-gram kmeans.png (clustering by 
grammatical aspects: error curves obtained for applying elbow method to 
determinate the optimal number of clusters)&lt;br&gt;* cube-gramm kmeans.png 
(clustering by grammatical aspects: a three-dimensional representation 
of  clusters obtained after applying k-means method)&lt;br&gt;* 
Grammatical_clusters (table) kmeans.xls (clustering by grammatical 
aspects: centroids, standard deviations and number of instances assigned
 to each cluster)&lt;br&gt;* elbow-lexgram kmeans.png (clustering by lexical 
and grammatical aspects: error curves obtained for applying elbow method
 to determinate the optimal number of clusters)&lt;br&gt;* 
Lexical_Grammatical_clusters (table) kmeans.xls (clustering by lexical 
and grammatical aspects:  centroids, standard deviations and number of 
instances assigned to each cluster)&lt;br&gt;* 
Grammatical_clusters_number_of_words (table) kmeans.xls
:  number of words (from column 2 to 4) and sizes (last column) obtained
 per each cluster by applying k-means clustering  to grammatical error 
ratios.&lt;br&gt;* Lexical_clusters_number_of_words (table) kmeans.xls
:  number of words (from column 2 to 4) and sizes (last column) obtained
 per each cluster by applying k-means clustering  to lexical error 
ratios.&lt;br&gt;* Lexical_Grammatical_clusters_number_of_words (table) 
kmeans.xls
:  number of words (from column 2 to 4) and sizes (last column) obtained
 per each cluster by applying k-means clustering  to lexical and 
grammatical error ratios.&lt;br&gt;&lt;br&gt;3. Ward’s Agglomerative Hierarchical Clustering files:&lt;br&gt;*
 Lexical_Cluster_Dendrogram_ward.png (clustering by lexical aspects: 
dendrogram obtained after applying Ward's clustering method).&lt;br&gt;* 
Grammatical_Cluster_Dendrogram_ward.png (clustering by grammatical 
aspects: dendrogram obtained after applying Ward's clustering method)&lt;br&gt;*
 Lexical_Grammatical_Cluster_Dendrogram_ward.png (clustering by lexical 
and grammatical aspects: dendrogram obtained after applying Ward's 
clustering method)&lt;br&gt;* Lexical_Grammatical_clusters (table) ward.xls:  
Centroids (from column 2 to 7) and cluster sizes (last column) obtained 
by applying Ward's agglomerative hierarchical clustering  to lexical and
 grammatical error ratios.&lt;br&gt;* Grammatical_clusters (table) ward.xls: 
Centroids (from column 2 to 4) and cluster sizes (last column) obtained 
by applying Ward's agglomerative hierarchical clustering  to grammatical
 error ratios.&lt;br&gt;* Lexical_clusters (table) ward.xls: Centroids (from 
column 2 to 4) and cluster sizes (last column) obtained by applying 
Ward's agglomerative hierarchical clustering  to lexical error ratios.&lt;br&gt;*
 Lexical_clusters_number_of_words (table) ward.xls: number of words 
(from column 2 to 4) and sizes (last column) obtained per each cluster 
by applying Ward's agglomerative hierarchical clustering  to lexical 
error ratios.&lt;br&gt;* Grammatical_clusters_number_of_words (table) 
ward.xls: number of words (from column 2 to 4) and sizes (last column) 
obtained per each cluster by applying Ward's agglomerative hierarchical 
clustering  to grammatical error ratios.&lt;br&gt;* 
Lexical_Grammatical_clusters_number_of_words (table) ward.xls: number of
 words (from column 2 to 4) and sizes (last column) obtained per each 
cluster by applying Ward's agglomerative hierarchical clustering  to 
lexical and grammatical error ratios.</description>
  </descriptions>
  <subjects>
    <subject schemeURI="http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E" subjectScheme="FOR">130309 Learning Sciences</subject>
    <subject schemeURI="http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E" subjectScheme="FOR">200307 German Language</subject>
    <subject schemeURI="http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E" subjectScheme="FOR">80109 Pattern Recognition and Data Mining</subject>
  </subjects>
  <publisher>figshare</publisher>
  <publicationYear>2018</publicationYear>
  <dates>
    <date dateType="Created">2018-04-27</date>
    <date dateType="Updated">2018-04-30</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset">Dataset</resourceType>
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