Beneficios de la aplicación del paradigma de líneas de productos software para generar dashboards en contextos educativos

  1. Andrea Vázquez-Ingelmo 1
  2. Roberto Therón 1
  1. 1 Universidad de Salamanca, USAL (España)
Revue:
RIED: revista iberoamericana de educación a distancia

ISSN: 1138-2783

Année de publication: 2020

Titre de la publication: Analítica del aprendizaje y educación basada en datos: Un campo en expansión

Volumen: 23

Número: 2

Pages: 169-185

Type: Article

DOI: 10.5944/RIED.23.2.26389 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: RIED: revista iberoamericana de educación a distancia

Résumé

Data are crucial to improve decision-making and to obtain greater benefits in any type of activity. However, the large amount of information generated by new technologies has made data analysis and knowledge generation a complex task. Numerous tools have emerged to facilitate this knowledge generation, such as dashboards. Although dashboards are very powerful tools, their effectiveness can be affected by a bad design or by not taking into account the context in which they are placed. Therefore, it is necessary to design and create tailored dashboards according to the audience and data domain. Creating tailored dashboards can be very beneficial, but also a costly process in terms of time and resources. This paper presents an application of the software product line paradigm to generate dashboards adapted to any context in a more straightforward way by reusing both software components and knowledge. One of the contexts that can be especially favored by this approach is the educational context, where Learning Analytics and the analysis of student performance to improve learning methodologies are becoming very popular. Having tailored dashboards for any role (student, teacher, administrator, etc.) can improve decision making processes by showing each user the information that interests them most in the way that best enables them to understand it.

€ Voir financement

Information sur le financement

Esta investigación ha sido parcialmente financiada por el Ministro de Economía y Competitividad del Gobierno de España a través del Proyecto DEFINES (TIN2016-80172-R). Este trabajo ha sido apoyado por el Ministerio de Educación, Cultura y Deporte bajo una beca FPU (FPU17/03276)

Financeurs

Références bibliographiques

  • Albright, S. C., Winston, W. y Zappe, C. (2010). Data analysis and decision making. Cengage Learning.
  • Aldrich, F. y Sheppard, L. (2000). Graphicacy-the fourth'R'? Primary Science Review, 64, 8-11.
  • Anquetil, N., Grammel, B., Galvão, I., Noppen, J., Khan, S. S., Arboleda, H., . . . Garcia, A. (2008). Traceability for model driven, software product line engineering. Ponencia presentada en ECMDA Traceability Workshop Proceedings.
  • Clark, S. (2018). Render your first network configuration template using Python and Jinja2. Cisco Blogs. Recuperado de https://blogs.cisco.com/developer/network-configuration-template
  • Clements, P.y Northrop, L. (2002). Software product lines. Addison-Wesley.
  • Cooper, M. M. (2007). Data-driven education research. Science, 317(5842), 1168-1171. doi:10.1126/science.317.5842.1171
  • Dabbebi, I., Iksal, S., Gilliot, J.-M., May, M. y Garlatti, S. (2017). Towards Adaptive Dashboards for Learning Analytic: An Approach for Conceptual Design and implementation. Ponencia presentada en 9th International Conference on Computer Supported Education (CSEDU 2017), Porto, Portugal, 120-131. doi:10.5220/0006325601200131
  • Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304–317. doi:10.1504/IJTEL.2012.051816
  • Gacek, C. y Anastasopoules, M. (2001). Implementing product line variabilities. Ponencia presentada en ACM SIGSOFT Software Engineering Notes, New York, NY, USA. 26, 109-117. doi:10.1145/375212.375269
  • Gomaa, H. (2004). Designing Software Product Lines with UML: From Use Cases to Pattern-Based Software Architectures. Addison Wesley Longman Publishing Co., Inc.
  • Greifenberg, T., Müller, K., Roth, A., Rumpe, B., Schulze, C. y Wortmann, A. (2016). Modeling variability in template-based code generators for product line engineering. arXiv preprint arXiv:1606.02903.
  • Kang, K. C., Cohen, S. G., Hess, J. A., Novak, W. E. y Peterson, A. S. (1990). Feature-oriented domain analysis (FODA) feasibility study. Tech. rep. CMU/SEI-90-TR-21. Software Engineering Institute, Carnegie Mellon University.
  • Kintz, M., Kochanowski, M. y Koetter, F. (2017). Creating User-specific Business Process Monitoring Dashboards with a Model-driven Approach. Ponencia presentada en MODELSWARD. SCITEPRESS. 353-361. doi:10.5220/0006135203530361
  • Kirda, E. y Kerer, C. (2000). MyXML: An XML based template engine for the generation of flexible Web content. Ponencia presentada en WebNet World Conference on the WWW and Internet (pp. 317-322). Association for the Advancement of Computing in Education (AACE).
  • Kuzilek, J., Hlosta, M. y Zdrahal, Z. (2017). Open university learning analytics dataset. Scientific data, 4:170171. 1-8. doi:10.1038/sdata.2017.171
  • Liñán, L. C. y Pérez, Á. A. J. (2015). Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. International Journal of Educational Technology in Higher Education, 12(3), 98-112. doi:10.7238/rusc.v12i3.2515
  • Logre, I., Mosser, S., Collet, P. y Riveill, M. (2014). Sensor data visualisation: a composition-based approach to support domain variability. Ponencia presentada en European Conference on Modelling Foundations and Applications. 101-116. doi:10.1007/978-3-319-09195-2_7
  • Long, P. D. y Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. Educause Review, 46(5), 30-32. doi:10.17471/2499-4324/195
  • Magdalenić, I., Radošević, D. y Kermek, D. (2011). Implementation Model of Source Code Generator. Journal of Communications and Software Systems. 7(2). 71-79. doi: 10.24138/jcomss.v7i2.180
  • Mandinach, E. B. y Honey, M. (2008). Data-driven school improvement: Linking data and learning. Teachers College Press.
  • Metzger, A. y Pohl, K. (2007). Variability management in software product line engineering. Ponencia presentada en the Companion to the proceedings of the 29th International Conference on Software Engineering. 186-187. doi:10.1109/ICSECOMPANION.2007.83
  • Munzner, T. (2014). Visualization analysis and design. AK Peters/CRC Press.
  • Palpanas, T., Chowdhary, P., Mihaila, G. y Pinel, F. (2007). Integrated model-driven dashboard development. Information Systems Frontiers, 9(2-3), 195-208. doi:10.1007/s10796-007-9032-9
  • Patil, D. y Mason, H. (2015). Data Driven: " O'Reilly Media, Inc.". ISBN: 9781491925454
  • Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9-20. doi:10.24059/olj.v16i3.267
  • Pleuss, A., Wollny, S. y Botterweck, G. (2013). Model-driven development and evolution of customized user interfaces. Ponencia presentada en Proceedings of the 5th ACM SIGCHI symposium on Engineering interactive computing systems,13-22. doi:10.1145/2494603.2480298
  • Pohl, K., Böckle, G. y Van der Linden, F. J. (2005). Software Product Line Engineering: Foundations, Principles and Techniques. Springer-Verlag New York, Inc.
  • Roberts, L. D., Howell, J. A. y Seaman, K. (2017). Give me a customizable dashboard: Personalized learning analytics dashboards in higher education. Technology, Knowledge and Learning, 22(3), 317-333. doi:10.1007/s10758-017-9316-1
  • Ronacher, A. (2008). Jinja2 Documentation. Recuperado de: http://jinja.pocoo.org/docs/2.10/:
  • Sarikaya, A., Correll, M., Bartram, L., Tory, M. y Fisher, D. (2018). What Do We Talk About When We Talk About Dashboards? IEEE Transactions on Visualization Computer Graphics, 25(1), 682 - 692. doi: 10.1109/TVCG.2018.2864903
  • Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P.,
  • Boroujeni, M. S., Holzer, A., . . . Dillenbourg, P. (2017). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies, 10(1), 30-41. doi:10.1109/TLT.2016.2599522
  • Tajali, S. B., Corriveau, J.-P. y Shi, W. (2013). A Template-Based Approach to Modeling Variability. Paper presented at the Proceedings of the International Conference on Software Engineering Research and Practice (SERP).
  • Tatsubori, M. y Suzumura, T. (2009). HTML templates that fly: a template engine approach to automated offloading from server to client. Actas de 18th international conference on World wide web, 951-960. doi:10.1145/1526709.1526837
  • Teasley, S. D. (2017). Student facing dashboards: One size fits all? Technology, Knowledge and Learning, 22(3), 377-384. doi:10.1007/s10758-017-9314-3
  • Van Gurp, J., Bosch, J. y Svahnberg, M. (2001). On the notion of variability in software product lines. Ponencia presentada en Software Architecture, 2001. Proceedings. Working IEEE/IFIP Conference on Software Architecture, Amsterdam, Netherlands, 45-54. doi:10.1109/WICSA.2001.948406
  • Vázquez-Ingelmo, A., García-Holgado, A., García-Peñalvo, F. J. y Therón, R. (2019). Dashboard Meta-Model for Knowledge Management in Technological Ecosystem: A Case Study in Healthcare. Ponencia presentada en the UCAmI 2019, Toledo, Castilla-La Mancha, Spain. MDPI Proceedings 2019, 31(1), 44, 1-13. doi:10.3390/proceedings2019031044
  • Vázquez-Ingelmo, A., García-Peñalvo, F. J. y Therón, R. (2019a). Addressing Fine-Grained Variability in User-Centered Software Product Lines: A Case Study on Dashboards. Ponencia presentada en World Conference on Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 930, 855-864. doi:10.1007/978-3-030-16181-1_80
  • Vázquez-Ingelmo, A., García-Peñalvo, F. J. y Therón, R. (2019b). Capturing high-level requirements of information dashboards’ components through meta-modeling. Ponencia presentada en 7th International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM 2019), León, Spain, 815-821, ACM. doi:10.1145/3362789.3362837
  • Vázquez-Ingelmo, A., García-Peñalvo, F. J. y Therón, R. (2019c). Information Dashboards and Tailoring Capabilities - A Systematic Literature Review. IEEE Access, 7, 109673-109688. doi:10.1109/ACCESS.2019.2933472
  • Vázquez-Ingelmo, A., García-Peñalvo, F. J., Therón, R. y Conde González, M. Á. (2019). Extending a dashboard meta-model to account for users’ characteristics and goals for enhancing personalization. Ponencia presentada en Learning Analytics Summer Institute (LASI) Spain 2019,
  • Vigo, Spain. CEUR Workshop Proceedings Series, no. 2415, pp. 35-42. http://ceur-ws.org/Vol-2415/paper04.pdf
  • Yoo, Y., Lee, H., Jo, I.-H. y Park, Y. (2015). Educational dashboards for smart learning: Review of case studies. Emerging issues in smart learning (pp. 145-155): Springer.
La source de données: Dialnet