Evaluation of Learning Object Repositories with multi-criteria methods based on Quality Indicators
Abstract
Learning Object Repositories (LOR), are appropriate platforms for the storage of Learning Objects (LOs). They make it easier for educational resources to be found and reused. However, the wide variety of LORs and resources available makes it difficult for the user to find an LOR that is appropriate in terms of their needs. In this sense, this work presents a method for evaluating LORs that aims to offer the best alternatives according to the user's needs. The proposed method makes use of the Analytic Hierarchy Process (AHP) which aims, at the end, to rank the most suitable alternatives according to the user's requests due to a required set of Indicators and their values.
Keywords:
Learning Object Repositories, Quality Indicators, Analytic Hierarchy Process
References
[Braga 2015] Braga, J. C. (2015). Objetos de Aprendizagem Vol.1: introdução e fundamentos, volume 1 of Intera. Editora da UFABC, Santo Andre, SP.
[Cechinel et al. 2016] Cechinel, C., da Silva Camargo, S., Sicilia, M.-A., and Alonso, S. S. (2016). Mining models for automated quality assessment of learning objects. J. UCS, 22(1):94–113.
[Committee et al. 2002] Committee, L. T. S. et al. (2002). Ieee standard for learning object metadata. IEEE Standard, 1484(1):2007–04.
[Ince et al. 2019] ˙Ince, M., Yigit, T., and Is¸ık, A. H. (2019). A hybrid ahp-ga method for metadata-based learning object evaluation. Neural Computing and Applications,
31(1):671–681.
[Kurilovas 2013] Kurilovas, E. (2013). Mceqls approach in multi-criteria evaluation of quality of learning repositories. In Innovations in XML Applications and Metadata Management: Advancing Technologies, pages 96–117. IGI Global, Hershey, PA.
[Kurilovas et al. 2016] Kurilovas, E., Vinogradova, I., and Kubilinskiene, S. (2016). New mceqls fuzzy ahp methodology for evaluating learning repositories: a tool for technological development of economy. Technological and Economic Development of Economy, 22(1):142–155.
[Saaty 1980] Saaty, T. L. (1980). The analytic hierarchy process mcgraw-hill. New York, 324.
[Standardization 2016] Standardization, I. O. (2016). Systems and Software Engineering: Systems and Software Quality Requirements and Evaluation (SQuaRE): Measurement of System and Software Product Quality. ISO, Geneva, Switzerland.
[Yigit et al. 2014] Yigit, T., Isik, A. H., and Ince, M. (2014). Multi criteria decision making system for learning object repository. Procedia-Social and Behavioral Sciences,
141:813–816.
[Cechinel et al. 2016] Cechinel, C., da Silva Camargo, S., Sicilia, M.-A., and Alonso, S. S. (2016). Mining models for automated quality assessment of learning objects. J. UCS, 22(1):94–113.
[Committee et al. 2002] Committee, L. T. S. et al. (2002). Ieee standard for learning object metadata. IEEE Standard, 1484(1):2007–04.
[Ince et al. 2019] ˙Ince, M., Yigit, T., and Is¸ık, A. H. (2019). A hybrid ahp-ga method for metadata-based learning object evaluation. Neural Computing and Applications,
31(1):671–681.
[Kurilovas 2013] Kurilovas, E. (2013). Mceqls approach in multi-criteria evaluation of quality of learning repositories. In Innovations in XML Applications and Metadata Management: Advancing Technologies, pages 96–117. IGI Global, Hershey, PA.
[Kurilovas et al. 2016] Kurilovas, E., Vinogradova, I., and Kubilinskiene, S. (2016). New mceqls fuzzy ahp methodology for evaluating learning repositories: a tool for technological development of economy. Technological and Economic Development of Economy, 22(1):142–155.
[Saaty 1980] Saaty, T. L. (1980). The analytic hierarchy process mcgraw-hill. New York, 324.
[Standardization 2016] Standardization, I. O. (2016). Systems and Software Engineering: Systems and Software Quality Requirements and Evaluation (SQuaRE): Measurement of System and Software Product Quality. ISO, Geneva, Switzerland.
[Yigit et al. 2014] Yigit, T., Isik, A. H., and Ince, M. (2014). Multi criteria decision making system for learning object repository. Procedia-Social and Behavioral Sciences,
141:813–816.
Published
2020-11-24
How to Cite
SILVA, Rosymeire Oliveira da; FIORESE, Adriano.
Evaluation of Learning Object Repositories with multi-criteria methods based on Quality Indicators. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 992-1001.
DOI: https://doi.org/10.5753/cbie.sbie.2020.992.
