Dashboards and visualisation tools for enhancing creativity in business master students

[EN] Dashboards are a basic element in Data Science. Well planned dashboards help the staff of a company at all levels of the organization. They allow them to ask questions and respond them in real time. As a result, this information allows them to make appropriate decisions and facilitates innovation. A fundamental component in the dashboards are the visualizations by means of dynamic graphic objects that can be explored. These visualizations must be analyzed dynamically so that business master students can intuitively arrive at a series of insights that bring them closer to the nature of the problems. Learning by doing and consulting. We are going to use a dashboard about innovation elaborated by Bankinter Fundation in the Platform Google Data Analytics. The proposed teaching dynamic includes the formation of work teams of 5-7 students. The challenge start when each group pose several questions to the rest of the teams. To answer these questions the students must consult the proposed dashboard. There is a time limit to answer each question. The winner is the team that answers correctly more questions and explains the way to obtain this information. This way, students get used to dashboards and visualisation tools and start learning with a good dashboard model that prepares them to later select and design proper tools. As a further result, we have appreciated that using visualisation in teaching can increase student engagement and performance.González-Ladrón-De-Guevara, F.; Fernández-Diego, M. (2021). Dashboards and visualisation tools for enhancing creativity in business master students. IATED. 8799-8804. https://doi.org/10.21125/inted.2021.1836S8799880

Application of mutual information-based sequential feature selection to ISBSG mixed data

[EN] There is still little research work focused on feature selection (FS) techniques including both categorical and continuous features in Software Development Effort Estimation (SDEE) literature. This paper addresses the problem of selecting the most relevant features from ISBSG (International Software Benchmarking Standards Group) dataset to be used in SDEE. The aim is to show the usefulness of splitting the ranked list of features provided by a mutual information-based sequential FS approach in two, regarding categorical and continuous features. These lists are later recombined according to the accuracy of a case-based reasoning model. Thus, four FS algorithms are compared using a complete dataset with 621 projects and 12 features from ISBSG. On the one hand, two algorithms just consider the relevance, while the remaining two follow the criterion of maximizing relevance and also minimizing redundancy between any independent feature and the already selected features. On the other hand, the algorithms that do not discriminate between continuous and categorical features consider just one list, whereas those that differentiate them use two lists that are later combined. As a result, the algorithms that use two lists present better performance than those algorithms that use one list. Thus, it is meaningful to consider two different lists of features so that the categorical features may be selected more frequently. We also suggest promoting the usage of Application Group, Project Elapsed Time, and First Data Base System features with preference over the more frequently used Development Type, Language Type, and Development Platform.Fernández-Diego, M.; González-Ladrón-De-Guevara, F. (2018). Application of mutual information-based sequential feature selection to ISBSG mixed data. Software Quality Journal. 26(4):1299-1325. https://doi.org/10.1007/s11219-017-9391-5S12991325264Angelis, L., & Stamelos, I. (2000). A simulation tool for efficient analogy based cost estimation. Empirical Software Engineering, 5(1), 35–68. https://doi.org/10.1023/A:1009897800559 .Auer, M., Trendowicz, A., Graser, B., Haunschmid, E., & Biffl, S. (2006). Optimal project feature weights in analogy-based cost estimation: improvement and limitations. Software Engineering, IEEE Transactions on, 32(2), 83–92.Awada, W., Khoshgoftaar, T. M., Dittman, D., Wald, R., Napolitano, A. (2012). A review of the stability of feature selection techniques for bioinformatics data. In 2012 I.E. 13th International Conference on Information Reuse and Integration (IRI) (pp. 356–363). Presented at the 2012 I.E. 13th International Conference on Information Reuse and Integration (IRI). https://doi.org/10.1109/IRI.2012.6303031 .Battiti, R. (1994). Using mutual information for selecting features in supervised neural net learning. Neural Networks, IEEE Transactions, 5(4), 537–550.Bennasar, M., Hicks, Y., & Setchi, R. (2015). Feature selection using joint mutual information maximisation. Expert Systems with Applications, 42(22), 8520–8532. https://doi.org/10.1016/j.eswa.2015.07.007 .Bibi, S., Tsoumakas, G., Stamelos, I., & Vlahavas, I. (2008). Regression via classification applied on software defect estimation. Expert Systems with Applications, 34(3), 2091–2101. https://doi.org/10.1016/j.eswa.2007.02.012 .Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28.Chatzipetrou, P., Papatheocharous, E., Angelis, L., Andreou, A. S. (2012). An investigation of software effort phase distribution using compositional data analysis. In 2012 38th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA) (pp. 367–375). Presented at the 2012 38th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA). https://doi.org/10.1109/SEAA.2012.50 .Chen, Z., Menzies, T., Port, D., & Boehm, B. (2005). Feature subset selection can improve software cost estimation accuracy. In Proceedings of the 2005 workshop on predictor models in software engineering (pp. 1–6). New York: ACM. https://doi.org/10.1145/1082983.1083171 .Chiu, N.-H., & Huang, S.-J. (2007). The adjusted analogy-based software effort estimation based on similarity distances. Journal of Systems and Software, 80(4), 628–640.Dash, M., & Liu, H. (2003). Consistency-based search in feature selection. Artificial Intelligence, 151(1), 155–176.Dejaeger, K., Verbeke, W., Martens, D., & Baesens, B. (2012). Data mining techniques for software effort estimation: a comparative study. Software Engineering, IEEE Transactions on, 38(2), 375–397. https://doi.org/10.1109/TSE.2011.55 .Deng, K., & MacDonell, S. G. (2008). Maximising data retention from the ISBSG repository. In Proceedings of the 12th international conference on evaluation and assessment in software engineering (pp. 21–30). Swinton: British Computer Society http://dl.acm.org/citation.cfm?id=2227115.2227118 . Accessed 21 Jan 2014.Doquire, G., & Verleysen, M. (2011). An hybrid approach to feature selection for mixed categorical and continuous data. In International Conference on Knowledge Discovery and Information Retrieval. http://hdl.handle.net/2078.1/90765 . Accessed 2 Nov 2015.Dudani, S. A. (1976). The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man and Cybernetics, SMC, 6(4), 325–327. https://doi.org/10.1109/TSMC.1976.5408784 .Estévez, P. A., Tesmer, M., Perez, C. A., & Zurada, J. M. (2009). Normalized mutual information feature selection. IEEE Transactions on Neural Networks, 20(2), 189–201. https://doi.org/10.1109/TNN.2008.2005601 .Fayyad, U.M., & Irani, K.B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In Proceedings of the International Joint Conference on Uncertainty in AI (pp. 1022–1027). Presented at the International Joint Conference on Uncertainty in AI. https://www.researchgate.net/publication/220815890_Multi-Interval_Discretization_of_Continuous-Valued_Attributes_for_Classification_Learning . Accessed 22 June 2016.Fernández-Diego, M., & González-Ladrón-de-Guevara, F. (2014). Potential and limitations of the ISBSG dataset in enhancing software engineering research: a mapping review. Information and Software Technology, 56(6), 527–544. https://doi.org/10.1016/j.infsof.2014.01.003 .Ferreira, A., & Figueiredo, M. (2011). Unsupervised joint feature discretization and selection. In J. Vitrià, J. M. Sanches, & M. Hernández (Eds.), Pattern recognition and image analysis (Vol. 6669, pp. 200–207). Berlin, Heidelberg: Springer Berlin Heidelberg http://link.springer.com/10.1007/978-3-642-21257-4_25 . Accessed 4 Mar 2016.Fleuret, F. (2004). Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research, 5, 1531–1555.González-Ladrón-de-Guevara, F., Fernández-Diego, M., & Lokan, C. (2016). The usage of ISBSG data fields in software effort estimation: a systematic mapping study. Journal of Systems and Software, 113, 188–215. https://doi.org/10.1016/j.jss.2015.11.040 .Gupta, P., Jain, S., & Jain, A. (2014). A review of fast clustering-based feature subset selection algorithm. International Journal of Scientific & Technology Research, 3(11), 86–91.Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157–1182.Hall, M. A., & Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering, 15(6), 1437–1447. https://doi.org/10.1109/TKDE.2003.1245283 .Hausser, J., & Strimmer, K. (2009). Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks. Journal of Machine Learning Research, 10(Jul), 1469–1484.Hill, P. (2010). Practical software project estimation: a toolkit for estimating software development effort & duration. McGraw Hill Professional.Hsu, H.-H., Hsieh, C.-W., & Lu, M.-D. (2011). Hybrid feature selection by combining filters and wrappers. Expert Systems with Applications, 38(7), 8144–8150.Huang, S.-J., & Chiu, N.-H. (2006). Optimization of analogy weights by genetic algorithm for software effort estimation. Information and Software Technology, 48(11), 1034–1045. https://doi.org/10.1016/j.infsof.2005.12.020 .Huang, S.-J., Chiu, N.-H., & Liu, Y.-J. (2008). A comparative evaluation on the accuracies of software effort estimates from clustered data. Information and Software Technology, 50(9–10), 879–888. https://doi.org/10.1016/j.infsof.2008.02.005 .Huang, J., Li, Y.-F., & Xie, M. (2015). An empirical analysis of data preprocessing for machine learning-based software cost estimation. Information and Software Technology, 67, 108–127. https://doi.org/10.1016/j.infsof.2015.07.004 .ISBSG. (2013a). ISBSG Dataset Release 12. ISBSG. http://isbsg.org/ . Accessed 1 Mar 2016.ISBSG. (2013b). ISBSG Guidelines Release 12.ISBSG. (2013c). ISBSG Data Demographics Release 12.Jeffery, R., Ruhe, M., Wieczorek, I. (2001). Using public domain metrics to estimate software development effort. In Software Metrics Symposium, 2001. METRICS 2001. Proceedings. Seventh International (pp. 16–27). https://doi.org/10.1109/METRIC.2001.915512 .Jiang, Z., & Comstock, C. (2007). The factors significant to software development productivity. In C. Ardil (Ed.), Proceedings of World Academy of Science, Engineering and Technology, Vol 19 (Vol. 19, pp. 160–164). Presented at the Conference of the World-Academy-of-Science-Engineering-and-Technology, Bangkok: World Acad Sci, Eng & Tech-Waset.Jørgensen, M., Indahl, U., & Sjøberg, D. (2003). Software effort estimation by analogy and ‘regression toward the mean’. Journal of Systems and Software, 68(3), 253–262. https://doi.org/10.1016/S0164-1212(03)00066-9 .Kabir, M. M., Shahjahan, M., & Murase, K. (2011). A new local search based hybrid genetic algorithm for feature selection. Neurocomputing, 74(17), 2914–2928.Kadoda, G., Cartwright, M., Chen, L., Shepperd, M. (2000). Experiences using case-based reasoning to predict software project effort. In EASE 2000 (pp. 2–3). Presented at the EASE 2000, Staffordshire, UK.Keung, J., Kocaguneli, E., & Menzies, T. (2012). Finding conclusion stability for selecting the best effort predictor in software effort estimation. Automated Software Engineering, 20(4), 543–567. https://doi.org/10.1007/s10515-012-0108-5 .Kirsopp, C., Shepperd, M. J., Hart, J. (2002). Search heuristics, case-based reasoning and software project effort prediction. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 9–13). New York, USA. http://v-scheiner.brunel.ac.uk/handle/2438/1554 . Accessed 27 Jan 2016.Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1–2), 273–324. https://doi.org/10.1016/S0004-3702(97)00043-X .Kwak, N., & Choi, C.-H. (2002). Input feature selection for classification problems. IEEE Transactions on Neural Networks, 13(1), 143–159. https://doi.org/10.1109/72.977291 .Langdon, W. B., Dolado, J., Sarro, F., & Harman, M. (2016). Exact mean absolute error of baseline predictor, MARP0. Information and Software Technology, 73, 16–18. https://doi.org/10.1016/j.infsof.2016.01.003 .Li, Y. F., Xie, M., & Goh, T. N. (2009). A study of mutual information based feature selection for case based reasoning in software cost estimation. Expert Systems with Applications, 36(3), 5921–5931.Liu, H., & Motoda, H. (2012). Feature selection for knowledge discovery and data mining (Vol. 454). Springer Science & Business Media. https://books.google.es/books?hl=en&lr=&id=aaDbBwAAQBAJ&oi=fnd&pg=PP10&dq=Feature+selection+for+knowledge+discovery+and+data+mining&ots=iuMhcWZGcf&sig=KlmNEIcsBdDVs-m1HUuICfpYZiM . Accessed 25 Jan 2016.Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4), 491–502. https://doi.org/10.1109/TKDE.2005.66 .Liu, H., Wei, R., & Jiang, G. (2013). A hybrid feature selection scheme for mixed attributes data. Computational and Applied Mathematics, 32(1), 145–161. https://doi.org/10.1007/s40314-013-0019-5 .Liu, Q., Wang, J., Xiao, J., Zhu, H. (2014). Mutual information based feature selection for symbolic interval data. In International Conference on Software Intelligence Technologies and Applications International Conference on Frontiers of Internet of Things 2014 (pp. 62–69). Presented at the International Conference on Software Intelligence Technologies and Applications International Conference on Frontiers of Internet of Things 2014. https://doi.org/10.1049/cp.2014.1537 .Lokan, C. (2005). What should you optimize when building an estimation model? In Software Metrics, 2005. 11th IEEE International Symposium (pp. 1–10). https://doi.org/10.1109/METRICS.2005.55 .Lokan, C., & Mendes, E. (2009a). Investigating the use of chronological split for software effort estimation. Software, IET, 3(5), 422–434. https://doi.org/10.1049/iet-sen.2008.0107 .Lokan, C., & Mendes, E. (2009b). Applying moving windows to software effort estimation. In Proceedings of the 2009 3rd international symposium on empirical software engineering and measurement (pp. 111–122). Washington, DC: IEEE Computer Society. https://doi.org/10.1109/ESEM.2009.5316019 .Lokan, C., & Mendes, E. (2012). Investigating the use of duration-based moving windows to improve software effort prediction. In Software Engineering Conference (APSEC), 2012 19th Asia-Pacific (Vol. 1, pp. 818–827). Presented at the Software Engineering Conference (APSEC), 2012 19th Asia-Pacific. https://doi.org/10.1109/APSEC.2012.74 .Lustgarten, J.L., Visweswaran, S., Grover, H., Gopalakrishnan, V. (2008). An evaluation of discretization methods for learning rules from biomedical datasets. In BIOCOMP (pp. 527–532).Mandal, M., & Mukhopadhyay, A. (2013). An improved minimum redundancy maximum relevance approach for feature selection in gene expression data. Procedia Technology, 10, 20–27. https://doi.org/10.1016/j.protcy.2013.12.332 .Mendes, E., Watson, I., Triggs, C., Mosley, N., & Counsell, S. (2003). A comparative study of cost estimation models for web hypermedia applications. Empirical Software Engineering, 8(2), 163–196.Mendes, E., Lokan, C., Harrison, R., Triggs, C. (2005). A replicated comparison of cross-company and within-company effort estimation models using the ISBSG database. In Software Metrics, 2005. 11th IEEE International Symposium (pp. 1–10). https://doi.org/10.1109/METRICS.2005.4 .Moses, J., Farrow, M., Parrington, N., & Smith, P. (2006). A productivity benchmarking case study using Bayesian credible intervals. Software Quality Journal, 14(1), 37–52. https://doi.org/10.1007/s11219-006-6000-4 .Núñez, H., Sànchez-Marrè, M., Cortés, U., Comas, J., Martínez, M., Rodríguez-Roda, I., & Poch, M. (2004). A comparative study on the use of similarity measures in case-based reasoning to improve the classification of environmental system situations. Environmental Modelling & Software, 19(9), 809–819. https://doi.org/10.1016/j.envsoft.2003.03.003 .Oh, I.-S., Lee, J.-S., & Moon, B.-R. (2004). Hybrid genetic algorithms for feature selection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(11), 1424–1437.Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238. https://doi.org/10.1109/TPAMI.2005.159 .R Core Team. (2015). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing https://www.R-project.org/ .Romanski, P., & Kotthoff, L. (2014). FSelector: Selecting attributes. R package version 0.20. https://CRAN.R-project.org/package=FSelector .Shannon, C. E. (1949). The mathematical theory of communication. Urbana: University of Illinois Press.Shepperd, M., & MacDonell, S. (2012). Evaluating prediction systems in software project estimation. Information and Software Technology, 54(8), 820–827.Shepperd, M., & Schofield, C. (1997). Estimating software project effort using analogies. Software Engineering, IEEE Transactions on, 23(11), 736–743.Somol, P., Pudil, P., & Kittler, J. (2004). Fast branch & bound algorithms for optimal feature selection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(7), 900–912.Song, Q., & Shepperd, M. (2007). A new imputation method for small software project data sets. Journal of Systems and Software, 80(1), 51–62.Top, O. O., Ozkan, B., Nabi, M., Demirors, O. (2011). Internal and External Software Benchmark Repository Utilization for Effort Estimation. In Software Measurement, 2011 Joint Conference of the 21st Int’l Workshop on and 6th Int’l Conference on Software Process and Product Measurement (IWSM-MENSURA) (pp. 302–307). https://doi.org/10.1109/IWSM-MENSURA.2011.41 .Vinh, L.T., Thang, N.D., Lee, Y.-K. (2010). An improved maximum relevance and minimum redundancy feature selection algorithm based on normalized mutual information. In 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT) (pp. 395–398). Presented at the 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT). https://doi.org/10.1109/SAINT.2010.50 .Witten, I.H., Frank, E., Hall, M.A., Pal, C.J. (2011). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann

Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review

Context The International Software Benchmarking Standards Group (ISBSG) maintains a software development repository with over 6000 software projects. This dataset makes it possible to estimate a project s size, effort, duration, and cost. Objective The aim of this study was to determine how and to what extent, ISBSG has been used by researchers from 2000, when the first papers were published, until June of 2012. Method A systematic mapping review was used as the research method, which was applied to over 129 papers obtained after the filtering process. Results The papers were published in 19 journals and 40 conferences. Thirty-five percent of the papers published between years 2000 and 2011 have received at least one citation in journals and only five papers have received six or more citations. Effort variable is the focus of 70.5% of the papers, 22.5% center their research in a variable different from effort and 7% do not consider any target variable. Additionally, in as many as 70.5% of papers, effort estimation is the research topic, followed by dataset properties (36.4%). The more frequent methods are Regression (61.2%), Machine Learning (35.7%), and Estimation by Analogy (22.5%). ISBSG is used as the only support in 55% of the papers while the remaining papers use complementary datasets. The ISBSG release 10 is used most frequently with 32 references. Finally, some benefits and drawbacks of the usage of ISBSG have been highlighted. Conclusion This work presents a snapshot of the existing usage of ISBSG in software development research. ISBSG offers a wealth of information regarding practices from a wide range of organizations, applications, and development types, which constitutes its main potential. However, a data preparation process is required before any analysis. Lastly, the potential of ISBSG to develop new research is also outlined.Fernández Diego, M.; González-Ladrón-De-Guevara, F. (2014). Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review. Information and Software Technology. 56(6):527-544. doi:10.1016/j.infsof.2014.01.003S52754456

Fotovaporización con láser verde para el tratamiento de la hiperplasia benigna de próstata

La hiperplasia benigna de próstata (HBP) es un proceso patológico benigno de proliferación celular que lleva al crecimiento del tamaño prostático y así al aumento de resistencia de salida al flujo miccional; lo que contribuye a los síntomas del tracto urinario (STUI) de los hombres de edad avanzada. Esto produce una afectación de la calidad de vida de los pacientes que la padecen hasta, en sus casos más avanzados, precisar una sonda vesical. El tratamiento es escalonado, disponiendo en el momento actual de fármacos muy efectivos que mejoran de forma significativa la calidad de vida e incluso previenen las complicaciones de la historia natural de la enfermedad. Sin embargo, cuando éstas aparecen o cuando el paciente lo desea el tratamiento más efectivo es el quirúrgico. El tratamiento quirúrgico convencional, resección transuretral de próstata (RTUp) o adenomectomía abierta, es muy efectivo para tratar los síntomas y resolver la HBP. Sin embargo, las tasas de complicaciones del propio tratamiento son altas. Por tanto desde hace décadas se busca una terapia quirúrgica alternativa lo suficientemente efectiva y más segura para el paciente. La Fotovaporización prostática con Láser de 532nm de longitud de onda que emite una característica luz verde (?GreenLigth?) es una de las técnicas más utilizadas desde hace más de 10 años. Y, aunque existen evidencias de su efectividad, los tratamientos quirúrgicos, a diferencia, por ejemplo de tratamientos medicamentosos, están sometidos a variables en su aplicación como la curva de aprendizaje, la experiencia del equipo quirúrgico etc; por lo que la valoración de la eficacia y seguridad de un tratamiento quirúrgico determinado se debe hacer sobre condiciones de práctica habitual.Así, realizamos un estudio de cohortes retrospectivo de 268 pacientes intervenidos con Laser Verde de manera uniforme en el Servicio de Urología del Hospital San Rafael de Madrid desde el año 2004, con un seguimiento medio de 3 años, una mediana de edad de 66 años y un volumen prostático mediano de 60ml (CI95%:62,21-70,93) de los cuales 74(28%) eran portadores de sonda vesical a permanencia, quedando todos libres de la misma al final del seguimiento.Conseguimos demostrar su eficacia al conseguir mejorías de los síntomas urinarios y su afectación en la calidad de vida mediante cuestionario de cuantificación validado (IPSS); así como mejorías en el flujo miccional máximo al final del seguimiento con diferencias estadísticamente significativas con p<0,0001.Presentamos una baja tasa de complicaciones como sangrado o incontinencia urinaria; con 20 pacientes que precisaron reintervención (7,5%) al final del seguimiento. Al estudiar los factores predictivos de complicaciones mediante modelo univariante y de regresión logística demostramos que el volumen prostático preoperatorio es un factor predictivo (RR=1,029 CI95%: 1,001-1,058) y sin embargo no lo es la diferencia entre el volumen prostático postoperatorio y preoperatorio

Diigo: Un marcador social de recursos Web

Descripción de las funcionalidades de los SMS (Sistemas de marcadores sociales) y de Diigo V.5. Utilidades para el aprendizaje colaborativoGonzález Ladrón De Guevara, FR. (2011). Diigo: Un marcador social de recursos Web. http://hdl.handle.net/10251/1078

The usage of ISBSG data fields in software effort estimation: A systematic mapping study

[EN] The International Software Benchmarking Standards Group (ISBSG) maintains a repository of data about completed software projects. A common use of the ISBSG dataset is to investigate models to estimate a software project's size, effort, duration, and cost. The aim of this paper is to determine which and to what extent variables in the ISBSG dataset have been used in software engineering to build effort estimation models. For that purpose a systematic mapping study was applied to 107 research papers, obtained after a filtering process, that were published from 2000 until the end of 2013, and which listed the independent variables used in the effort estimation models. The usage of ISBSG variables for filtering, as dependent variables, and as independent variables is described. The 20 variables (out of 71) mostly used as independent variables for effort estimation are identified and analysed in detail, with reference to the papers and types of estimation methods that used them. We propose guidelines that can help researchers make informed decisions about which ISBSG variables to select for their effort estimation models.González-Ladrón-De-Guevara, F.; Fernández-Diego, M.; Lokan, C. (2016). The usage of ISBSG data fields in software effort estimation: A systematic mapping study. Journal of Systems and Software. 113:188-215. doi:10.1016/j.jss.2015.11.040S18821511

Diigo: social bookmarking, basic support for collaborative learning and research

La web 2.0 ha originado nuevas aplicaciones, tales como los Sistemas de Marcadores Sociales (SBS) con una marcada función socializadora, centrada no tanto en las relaciones entre los usuarios como en proporcionarles las herramientas necesarias para manejar y gestionar información que posteriormente puede ser compartida. En el presente artículo se definen los SBS como aplicaciones web que ayudan a almacenar, clasificar, organizar, describir y compartir información multiformato mediante enlaces a páginas web, blogs, imágenes, wikis, vídeos y podcasts de interés, destacando sus ventajas para favorecer el trabajo grupal. En concreto, se estudia Diigo y sus aportaciones como herramienta metacognitiva, que permite visibilizar el modo de aprender, pensar y elaborar el conocimiento que cada sujeto posee a partir de la información que selecciona, organiza y categoriza, incrementando su valor al compartirla. Facilita el aprendizaje y la investigación colaborativa, al compartir las etiquetas que describen los recursos convirtiéndolos en unidades valiosas. Propicia la cohexión de grupos de investigación mediante la navegación por la información referenciada y etiquetada por otros, a la que cualquiera puede suscribirse y reetiquetar incorporando otros matices. Favorece la gestión de la información recabada en diferentes fases de una investigación. En definitiva, propicia el trabajo colaborativo al unir las sinérgias de un determinado grupo de investigación, agilizando la difusión de ideas entre campos interdisciplinares y contribuyendo a la construcción colectiva del conocimiento.Web 2.0 has originated new applications, like the Social Bookmarking Systems (SBS) with remarkable socializing features, Rather than focusing on the relationship between users, it provides users with the necessary tools to manage and use information that can be later shared. In this article SBS are defined as web applications that can help to store, classify, organize, describe and share multi-format information through links to web sites, blogs, images, wikis, videos and podcasts of interest, emphasizing their advantages for supporting collaborative work. Specifically, Diigo and its contributions will be studied as a metacognitive tool wich makes visible the way each user learns, thinks and develops the knowledge obtained from the information he selects, organizes and categorizes, incrementing its value when sharing it. It facilitates collaborative learning and research through the sharing of tags that describe marked resources giving them high value. Diigo favours the research groups connection thanks to the browsing of the referenced information that has been tagged by others. Everybody can suscribe himself to that information, re-tagging it and adding new nuances. Also favours the information management in the different research phases. Ultimately, it propitiates the collaborative work thanks to the union of the different synergies of the members team, speeding the broadcast of the ideas between interdisciplinary fields and contributing to the collective knowledge development

The use of timelines as a strategy for teaching legislation issues to IT engineering degree students

[EN] A timeline is a graph showing a sequence of events on a particular topic. Among other things, it allows to visualize a space-time relationship of the most relevant periods of the topic to be addressed. These timelines have always been valuable teaching strategies. However, with the use of multimedia resources linked to the timeline such as images or videos, there is a greater possibility that students will relate the contents to specific objects that will help them in the understanding and memorization of dates or events. The course Deontology and Professionalism in the degree of IT engineering at the Universitat Politècnica de València incorporates contents that enable students to be exposed to issues of professional practice, ethical conduct and computer legislation. When working on the legislation aspects, we suggest the use of timelines as a teaching-learning strategy. Specifically, the Spanish and European chronologies of data protection and intellectual property legislation are presented as an example. But if we really want to take advantage of this tool, there is nothing better than involving students in the creation of such timelines. In this way they will be much more involved and motivated.Fernández-Diego, M.; González-Ladrón-De-Guevara, F.; Ruiz Font, L.; Boza, A. (2021). The use of timelines as a strategy for teaching legislation issues to IT engineering degree students. IATED. 8678-8683. https://doi.org/10.21125/inted.2021.1798S8678868

Maximización de los beneficios de los sistemas ERP

The ERP (Enterprise Resource Planning) systems have been consolidated in companies with different sizes and sectors, allowing their real benefits to be definitively evaluated. In this study, several interactions have been studied in different phases, such as the strategic priorities and strategic planning defined as ERP Strategy; business processes review and the ERP selection in the pre-implementation phase, the project management and ERP adaptation in the implementation phase, as well as the ERP revision and integration efforts in the post-implementation phase. Through rigorous use of case study methodology, this research led to developing and to testing a framework for maximizing the benefits of the ERP systems, and seeks to contribute for the generation of ERP initiatives to optimize their performance.Los sistemas de Planificación de los Recursos Empresariales, ERP (Enterprise Resources Planning) se van consolidando en las empresas de diferentes tamaños y sectores, permitiendo que sus beneficios reales puedan ser definitivamente evaluados. En el presente estudio, varias interacciones han sido objeto de estudio en las distintas fases definidas, como las prioridades estratégicas y la planificación estratégica en la Estrategia ERP, la revisión de los procesos de negocio, y la selección del ERP en la fase de pre-implantación, las gestión de proyecto y la adaptación del ERP en la fase de implantación, así como la revisión del ERP, y los esfuerzos de integración en la fase de post implantación. A través del empleo de una metodología rigurosa de estudio de casos, el presente trabajo permitió elaborar y contrastar un modelo conceptual de maximización de los beneficios de los sistemas ERP y pretende ser una contribución para la generación de estrategias de iniciativas, que permitan optimizar su rendimiento

Twitter use in the Latin American universities

Las redes sociales ofrecen fórmulas eficaces a la institución de educación superior tanto para desarrollar actividades formativas innovadoras apoyadas en la participación e interacción entre docentes y estudiantes, como para mejorar su identidad corporativa, difundir y transmitir información institucional. La presente investigación analiza el uso de Twitter que hacen las 20 primeras universidades iberoamericanas (Ranking Shangai, 2012), mediante la identificación de sus listas, seguidores y la generación de tweets y retweets. Los resultados más significativos ponen de manifiesto por un lado, que algunos docentes la usan como herramienta catalizadora del proceso de enseñanza-aprendizaje a partir de los hashtags, y por otro, que a nivel institucional, las universidades lo hacen para dinamizar la participación e interacción con la comunidad educativa mediante los Tweets. El promedio de seguidores de las universidades estudiadas en Twitter es de 4.772 usuarios, y de 114 tweets usuarios (2011). La Universidad Nacional Autónoma de México es la que posee más (35.679). Casi todas ven la importancia del uso de Twitter, sin embargo se infrautiliza como canal de comunicación directa y efectiva. Los docentes deben actualizarse para manejar las TIC y las redes sociales, descubrir su aplicabilidad educativa y rentabilizar el tiempo de interacción con los estudiantes. Además, la comunicación entre las universidades y la comunidad educativa podría verse optimizada con Twitter si para el envío de mensajes efectivos aprovecharan sus bases de datos como canal de difusión viral.Social networks offer effective ways to higher education centers to develop innovative training activities supported by the participation and interaction between teachers and students and in the same way to improve their corporate identity, broadcasting and transmiting corporate information. This research analyzes the usage of top 20 Latin American Universities (Sanghai Ranking, 2012) that participate in Twitter by identifying their lists, fans and tweets and retweets. The most significant results show that some teachers use it as a tool that fosters the teaching and learning process via the hashtags, and furthermore, that at the institutional level, universities boost the participation and interaction with the educational community through tweets. The average fans level of the studied universities is 4,772 Twitter users considering tweets of 114 users (2011). The National Autonomous University of Mexico is the one with most (35,679) followers. Almost al of them see the importance of using Twitter, however Twitter is underused as a channel of direct and effective communication. Teachers should be trained to use ICT and social network to discover their educational applicability and to take advantage of interaction time with students. Furthermore, communication between universities and their educational communities could be optimized using Twitter to send effective messages to exploit their databases as a viral distribution channel