Show simple item record

Towards the Proposal of a Specific Domain Model for Educational Data Mining: Problem Identification

dc.contributor.authorHernandez-Leal E.J
dc.contributor.authorDuque-Mendez N.D.
dc.date.accessioned2022-09-14T14:34:17Z
dc.date.available2022-09-14T14:34:17Z
dc.date.created2021
dc.identifier.isbn9781665423588
dc.identifier.urihttp://hdl.handle.net/11407/7608
dc.descriptionThe use of data analysis techniques in educational contexts supports planning and decision-making. Data mining is an alternative that meets the current needs in data management in this field of study. However, most data mining tools and applications are geared towards general domains; they do not specialize in the problems or data inherent in this particular domain. This article presents an initial proposal for an educational data mining model with a specific domain approach to offer solution mechanisms to particular problems at each stage of the mining process and generic domain models in general. In this model iteration, the problems associated with the data were addressed through transformations from generic to a specific domain. © 2021 IEEE.eng
dc.language.isospa
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127178428&doi=10.1109%2fLACLO54177.2021.00054&partnerID=40&md5=7189faebe3eee3e5cb086f924c15e157
dc.sourceProceedings - 2021 16th Latin American Conference on Learning Technologies, LACLO 2021
dc.titleTowards the Proposal of a Specific Domain Model for Educational Data Mining: Problem Identification
dc.typeConference Paper
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Sistemas
dc.type.spaDocumento de conferencia
dc.identifier.doi10.1109/LACLO54177.2021.00054
dc.subject.keywordData Miningeng
dc.subject.keywordEducational Dataeng
dc.subject.keywordSpecific Domaineng
dc.subject.keywordDecision makingeng
dc.subject.keywordInformation managementeng
dc.subject.keywordIterative methodseng
dc.subject.keyword'currenteng
dc.subject.keywordData analysis techniqueseng
dc.subject.keywordData mining applicationseng
dc.subject.keywordData mining problemseng
dc.subject.keywordDecisions makingseng
dc.subject.keywordEducational contexteng
dc.subject.keywordEducational dataeng
dc.subject.keywordProblem identificationeng
dc.subject.keywordSpecific domaineng
dc.subject.keywordSpecific domain modeleng
dc.subject.keywordData miningeng
dc.relation.citationstartpage554
dc.relation.citationendpage557
dc.publisher.facultyFacultad de Ingenierías
dc.affiliationHernandez-Leal, E.J., Universidad De Medellín, Facultad De Ingenieriás, Medellín, Colombia
dc.affiliationDuque-Mendez, N.D., Universidad Nacional De Colombia, Facultad De Administración, Manizales, Colombia
dc.relation.referencesLarose, D.T., Larose, C.D., (2014) Discovering Knowledge in Data: An Introduction to Data Mining, , Second. Canada: John Wiley & Sons. Inc
dc.relation.referencesDavid, D.J., Hand, J., Mannila, H., Smyth, P., (2001) Principles of Data Mining, , MIT Press
dc.relation.referencesRomero, C., Ventura, S., Data mining in education (2013) Wiley Interdiscip. Rev. DataMin. Knowl. Discov., 3 (1), pp. 12-27. , Jan
dc.relation.referencesScheuer, O., McLaren, B.M., Educational Data Mining (2012) Encyclopedia of the Sciences of Learning, pp. 1075-1079. , Boston, MA: Springer US
dc.relation.referencesKosar, T., Comparing general-purpose and domain-specific languages: An empirical study (2010) Comput. Sci. Inf. Syst, 7 (2), pp. 247-264
dc.relation.referencesJimenez Celorrio, S., De La Turbides Rosa, T., Learning-Based Planning (2009) Encyclopedia of Artificial Intelligence, pp. 1024-1028. , Madrid: IGI Global
dc.relation.referencesKautz, H., Selman, B., The Role of Domain-Specific Knowledge in the Planning as Satisfiability Framework (1998) American Association for Artificial Intelligence, pp. 181-189. , http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1259, Accessed Oct 23 2017
dc.relation.referencesHiibscher, R., Puntambekar, S., Nye, A.H., Domain specific interactive data mining (2007) Workshop on Data Mining for User Modeling at the 11th International Conference on User Modeling, pp. 81-90
dc.relation.referencesCao, L., Zhang, C., The Evolution of KDD: Towards Domain-Driven Data Mining (2007) Int. J. Pattern Recognit. Artif. Intel!, 21 (4), pp. 677-692. , Jun
dc.relation.referencesCao, L., Domain-Driven Data Mining: Challenges and Prospects (2010) IEEE Trans. Knowl. Data Eng., 22 (6), pp. 755-769. , Jun
dc.relation.referencesChe, D., Safran, M., Peng, Z., From Big Data to Big Data Mining: Challenges, Issues, and Opportunities (2013) Database Systems for Advanced Applications: 18th International Conference, DASFAA 2013, International Workshops: BDMA, SNSM, SeCoP, pp. 1-15. , Wuhan, China, April 22-25, 2013. Proceedings, B. Hong, X. Meng, L. Chen, W. Winiwarter, and W. Song, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg
dc.relation.referencesVarde, A.S., Tatti, N., A Panorama of Imminent Doctoral Research in Data Mining (2014) ACM SIGMOD Rec, 43 (3), pp. 71-74. , Dec
dc.relation.referencesBegum, S.H., Data Mining Tools and Trends-An Overview (2013) Int. J. Emerg. Res. Manag. &Technology, pp. 6-12
dc.relation.referencesDsilva, C.J., Talmon, R., Gear, C.W., Coifman, R.R., Kevrekidis, I.G., Data-Driven Reduction for Multiscale Stochastic Dynamical Systems (2015) Appl. Dyn. Syst., Jan., , http://arxiv.org/abs/1501.05195, Accessed: Nov. 16 2017
dc.relation.referencesBermanis, A., Averbuh, A., Coifman, R., Multiscale data sampling and function extension (2013) Appl. Comput. Harmon. Anal., 34 (1), pp. 15-29. , Jan
dc.relation.referencesChen, G., Iwen, M., Chin, S., Maggioni, M., A fast multiscale framework for data in high-dimensions: Measure estimation, anomaly detection, and compressive measurements (2012) Visual Communications and Image Processing, pp. 1-6. , Nov
dc.relation.referencesDeFreitas, K., Bernard, M., A Framework for Flexible Educational Data Mining (2014) Proc. Int. Conf. DataMin.
dc.relation.referencesDe Neto Almeida, F.A., Castro, A., A reference architecture for educational data mining (2017) Proceedings-Frontiers in Education Conference, FIE, 2017, pp. 1-8. , Dec.-October
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/other
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín
dc.relation.ispartofconference6th Latin American Conference on Learning Technologies, LACLO 2021


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record