Application of data mining for the classification of university programs of industrial engineering accredited in high quality in Colombia
The present research article proposes a method to classify University engineering programs, placing special attention to relations between the subjects of the curriculum and the 12 areas of knowledge established in the body of competencies published by the Institute of industrial and System Engineer...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8729
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8729
- Palabra clave:
- Clustering
Data mining
Education
Industrial engineering
Principal components
Application programs
Cluster analysis
Data mining
Education
Industrial engineering
Industrial research
Clustering
Engineering program
International accreditation
Principal components
Principal components analysis
System engineers
University programs
Unsupervised data
Principal component analysis
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Application of data mining for the classification of university programs of industrial engineering accredited in high quality in Colombia |
| dc.title.alternative.none.fl_str_mv |
Aplicación de minería de datos para la clasificación de programas universitarios de ingeniería industrial acreditados en alta calidad en Colombia |
| title |
Application of data mining for the classification of university programs of industrial engineering accredited in high quality in Colombia |
| spellingShingle |
Application of data mining for the classification of university programs of industrial engineering accredited in high quality in Colombia Clustering Data mining Education Industrial engineering Principal components Application programs Cluster analysis Data mining Education Industrial engineering Industrial research Clustering Engineering program International accreditation Principal components Principal components analysis System engineers University programs Unsupervised data Principal component analysis |
| title_short |
Application of data mining for the classification of university programs of industrial engineering accredited in high quality in Colombia |
| title_full |
Application of data mining for the classification of university programs of industrial engineering accredited in high quality in Colombia |
| title_fullStr |
Application of data mining for the classification of university programs of industrial engineering accredited in high quality in Colombia |
| title_full_unstemmed |
Application of data mining for the classification of university programs of industrial engineering accredited in high quality in Colombia |
| title_sort |
Application of data mining for the classification of university programs of industrial engineering accredited in high quality in Colombia |
| dc.subject.keywords.none.fl_str_mv |
Clustering Data mining Education Industrial engineering Principal components Application programs Cluster analysis Data mining Education Industrial engineering Industrial research Clustering Engineering program International accreditation Principal components Principal components analysis System engineers University programs Unsupervised data Principal component analysis |
| topic |
Clustering Data mining Education Industrial engineering Principal components Application programs Cluster analysis Data mining Education Industrial engineering Industrial research Clustering Engineering program International accreditation Principal components Principal components analysis System engineers University programs Unsupervised data Principal component analysis |
| description |
The present research article proposes a method to classify University engineering programs, placing special attention to relations between the subjects of the curriculum and the 12 areas of knowledge established in the body of competencies published by the Institute of industrial and System Engineers (IIES). Techniques of unsupervised data analysis such as Principal Components Analysis (PCA) and cluster analysis were used for the proposed classification. Twenty-one programs, accredited by high quality in Industrial Engineering in Colombia, are used as units of study. The results show that factors such as international accreditation, size of the faculties of engineering and University profile, influence the grouping of the programs of study. The research allowed to classify three large main components and profiles of accredited programs. © 2018 Centro de Informacion Tecnologica. All Rights Reserved. |
| publishDate |
2018 |
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2018 |
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2019-11-06T19:05:11Z |
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2019-11-06T19:05:11Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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Artículo |
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Informacion Tecnologica; Vol. 29, Núm. 3; pp. 89-96 |
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0716-8756 |
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https://hdl.handle.net/20.500.12585/8729 |
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10.4067/S0718-07642018000300089 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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Informacion Tecnologica; Vol. 29, Núm. 3; pp. 89-96 0716-8756 10.4067/S0718-07642018000300089 Universidad Tecnológica de Bolívar Repositorio UTB |
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https://hdl.handle.net/20.500.12585/8729 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional |
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Centro de Informacion Tecnologica |
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Centro de Informacion Tecnologica |
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2019-11-06T19:05:11Z2019-11-06T19:05:11Z2018Informacion Tecnologica; Vol. 29, Núm. 3; pp. 89-960716-8756https://hdl.handle.net/20.500.12585/872910.4067/S0718-07642018000300089Universidad Tecnológica de BolívarRepositorio UTBThe present research article proposes a method to classify University engineering programs, placing special attention to relations between the subjects of the curriculum and the 12 areas of knowledge established in the body of competencies published by the Institute of industrial and System Engineers (IIES). Techniques of unsupervised data analysis such as Principal Components Analysis (PCA) and cluster analysis were used for the proposed classification. Twenty-one programs, accredited by high quality in Industrial Engineering in Colombia, are used as units of study. The results show that factors such as international accreditation, size of the faculties of engineering and University profile, influence the grouping of the programs of study. The research allowed to classify three large main components and profiles of accredited programs. © 2018 Centro de Informacion Tecnologica. All Rights Reserved.Center for Outcomes Research and Evaluation, Yale School of MedicineRecurso electrónicoapplication/pdfengCentro de Informacion Tecnologicahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85054936093&doi=10.4067%2fS0718-07642018000300089&partnerID=40&md5=1d03643e953a125091f273e7a7c8a3d3Scopus 57195394151Scopus 57204201834Scopus 57195395542Application of data mining for the classification of university programs of industrial engineering accredited in high quality in ColombiaAplicación de minería de datos para la clasificación de programas universitarios de ingeniería industrial acreditados en alta calidad en Colombiainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1ClusteringData miningEducationIndustrial engineeringPrincipal componentsApplication programsCluster analysisData miningEducationIndustrial engineeringIndustrial researchClusteringEngineering programInternational accreditationPrincipal componentsPrincipal components analysisSystem engineersUniversity programsUnsupervised dataPrincipal component analysisFontalvo Herrera, Tomás JoséDe la Hoz Domínguez, Enrique JoséMendoza-Mendoza, A.A.Arista-Jalife, A., Calderón-Auza, G., Fierro-Radilla, A., Nakano, M., Clasificación de Imágenes Urbanas Aéreas: Comparación entre Descriptores de Bajo Nivel y Aprendizaje Profundo (2017) Inf. Tecnol., 28 (3), pp. 209-224(1999) The Bologna Declaration of 19 June 1999, , https://www.eurashe.eu/library/bologna_1999_bologna-declaration-pdf/, en la web: acceso: 10 de Dicienbre 2016Bray, J., Curtis, J., An ordination of the upland foret communities of southern Wisconsin (1957) Ecological Monographs, 27 (4), pp. 325-349Breu, H., Gil, J., Kirlpatrick, D., Werman, M., Linear time euclidean distance transform algorithms (1995) IEE Transactions on Pattern Analysis and Machine Intelligence, 17 (5), pp. 529-533Cain, A., Harrison, G., An analysis of the taxonomist's judgment of affinity (1958) Journal of Zoology, 131 (1), pp. 85-98Cattel, R., The scree test for the number of factors (1966) Multivariate Behavioral Research, 1 (2), pp. 245-276Cuadras, C., (2014) Nuevos Métodos De Análisis Multivariante, pp. 138-140. , CMC Editions. 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Tecnol, 28 (4), pp. 67-74Dietrich, G., The emergence of the credit system in American education considered as a problem of social and intellectual history (1955) Bulletin of The American Association of University Professors (1915-1955, 41 (4), pp. 647-668Gower, J., A general coefficient of similarity and some of its properties (1971) Biometrics, 27 (4), pp. 857-871He, Y., Sang, N., Gao, C., Han, J., Online Unsupervised Learning Classification of pedestrian and vehicle for video surveillance (2017) Chinese Journal of Electronics, 26 (1), pp. 145-151Hotelling, H., Analysis of a complex of statistical variables into principal components (1933) Journal of Educational Pshychology, 24 (6), pp. 417-441Institute of Industrial and Systems Engineers, , http://www.iise.org, en la web: acceso 23 de Abril 2017Kotsiantis, S., Zaharakis, I., Pintelas, P., Supervised machine learning: A review of classification techniques (2007) Informatica, 31, pp. 249-268Lange, G., Williams, W.A., General theory of classificatory sorting strategies: II. Clustering Systems (1967) The Computer Journal, 10 (3), pp. 271-277Le, S., Josse, J., Husson, F., FactoMiner: A package for Multivariate Analysis (2008) Journal of Statistical Software, 25 (1), pp. 1-18Lohman, C., Fortuin, L., Marc, W., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-268Maffioli, F., Augusti, G., Tuning engineering education into the european higher education orchestra (2003) European Journal of Engineering Education, 28 (3), pp. 251-273Ibrahim, M.R., Williams, F., Concept maps: Development and validation of engineering curricula (2007) Frontiers In Education Conference-Global Engineering: Knowledge Without Borders, Opportunities Without Passports, pp. 518-523. , ilweakee, USA 10 a 13 de OctubrePassow, H., Passow, C., What competencies should undergraduate engineering programs emphasize? 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On the use of visual thinking in theory pictures, theorizing diagrams, and visual sketches (2016) Sociological Theory, 34 (3), pp. 250-275Tang, F., Hess, T., Valacich, J., Sweeney, J., The effects of visualization and interactivity on calibration in financial decision-making (2013) Behavioral Research in Accounting, 26 (1), pp. 25-28Tirado, L., Estrada, J., Ortiz, R., Solano, H., Gonzalez, J., Alfonso, D., Ortiz, D., Competencias profesionales: Una estrategia para el desempeño exitoso de los ingenieros industriales (2007) Revista Facultad De Ingeniería Universidad De Antioquia, pp. 123-139Verikas, A., Gelzinis, A., Bacauskiene, M., Mining data with random forests: A survey and results of new tests (2011) Pattern Recognition, 44 (2), pp. 330-349Xanthopoulos, A.S., Koulouriotis, D.E., Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing (2015) Journal of Intelligent Manufacturing, pp. 1-23http://purl.org/coar/resource_type/c_6501ORIGINALDOI10_4067S0718-07642018000300089.pdfapplication/pdf362053https://repositorio.utb.edu.co/bitstream/20.500.12585/8729/1/DOI10_4067S0718-07642018000300089.pdf3455448422ad8744a988f951e18f29adMD51TEXTDOI10_4067S0718-07642018000300089.pdf.txtDOI10_4067S0718-07642018000300089.pdf.txtExtracted texttext/plain34647https://repositorio.utb.edu.co/bitstream/20.500.12585/8729/4/DOI10_4067S0718-07642018000300089.pdf.txt8ddba41806605cf8a77d9015c6813541MD54THUMBNAILDOI10_4067S0718-07642018000300089.pdf.jpgDOI10_4067S0718-07642018000300089.pdf.jpgGenerated Thumbnailimage/jpeg86765https://repositorio.utb.edu.co/bitstream/20.500.12585/8729/5/DOI10_4067S0718-07642018000300089.pdf.jpg97ff60e88e8cdff6ff96fb61d1e72795MD5520.500.12585/8729oai:repositorio.utb.edu.co:20.500.12585/87292023-05-26 09:37:47.676Repositorio Institucional UTBrepositorioutb@utb.edu.co |