La tutorización proactiva como factor de mejora en los resultados de la formación online

In this research, we established the hypothesis that a proactive tutoring model, focused on the continuous monitoring and follow-up of the online student, improves the rate of approved students and student satisfaction. To validate this hypothesis, we describe and compare the results of the satisfaction surveys and the approved rate of the courses taught through the Coordinated Study Plan in Financial Advisory of the Center for Permanent Training of the Polytechnic University of Valencia. We included in the research 27 courses that follow a proactive tutoring model with a total of 5.613 students enrolled and a sample of 2.500 questionnaires and 30 courses that follow a reactive tutoring model with a total of 1.139 students enrolled with a sample of 583 questionnaires. The results obtained permit accepting the initial hypothesis, confirming that in the courses that a proactive tutoring model was followed, the approved rate is 27% higher and regarding student satisfaction in both types of tutoring the general pattern of responses to the survey, with certain differences in their distribution, were similar, although satisfaction is higher in courses that have followed a proactive tutoring model, while dissatisfaction is greater in those with a reactive tutoring.En el presente trabajo de investigación se plantea como hipótesis que un modelo de tutorización proactiva, centrado en el acompañamiento y seguimiento continuo del estudiante online, mejora la tasa de aprobados y la satisfacción de los estudiantes. Para validar dicha hipótesis se describen y comparan los resultados de las encuestas de satisfacción y la tasa de aprobados de los cursos impartidos a través del Plan de Estudios Coordinados en Asesoría Financiera del Centro de Formación Permanente de la Universitat Politècnica de València, incluyéndose en el estudio 27 cursos que siguen un modelo de tutorización proactiva con un total de 5.613 estudiantes matriculados y una muestra de 2.500 cuestionarios y 30 cursos que siguen un modelo de tutorización reactiva con un total de 1.862 estudiantes matriculados y una muestra de 583 cuestionarios. Los resultados obtenidos permiten aceptar la hipótesis de partida confirmando que en los cursos que se ha seguido un modelo de tutorización proactiva la tasa de aprobados es un 27% más alta, y al respecto de la satisfacción del alumnado, en ambos tipos de tutorización, el patrón general de respuestas a las encuestas, con ciertas diferencias en la distribución de las mismas, es similar si bien, la satisfacción es más alta en los cursos que han seguido un modelo de tutorización proactivo, mientras que la insatisfacción es mayor en los de tutorización reactiva

Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach

[EN] Sustainable finance, which integrates environmental, social and governance criteria on financial decisions rests on the fact that money should be used for good purposes. Thus, the financial sector is also expected to play a more important role to decarbonise the global economy. To align financial flows with a pathway towards a low-carbon economy, investors should be able to integrate into their financial decisions additional criteria beyond return and risk to manage climate risk. We propose a tri-criterion portfolio selection model to extend the classical Markowitz's mean-variance approach to include investor's preferences on the portfolio carbon risk exposure as an additional criterion. To approximate the 3D Pareto front we apply an efficient multi-objective genetic algorithm called ev-MOGA which is based on the concept of epsilon-dominance. Furthermore, we introduce a-posteriori approach to incorporate the investor's preferences into the solution process regarding their climate-change related preferences measured by the carbon risk exposure and their loss-adverse attitude. We test the performance of the proposed algorithm in a cross-section of European socially responsible investments open-end funds to assess the extent to which climate-related risk could be embedded in the portfolio according to the investor's preferences.Hilario Caballero, A.; Garcia-Bernabeu, A.; Salcedo-Romero-De-Ávila, J.; Vercher, M. (2020). Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach. International Journal of Environmental research and Public Health. 17(17):1-15. https://doi.org/10.3390/ijerph17176324S1151717Morningstar Low Carbon Designationhttps://bit.ly/2SfAFUAKrueger, P., Sautner, Z., & Starks, L. T. (2020). 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Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market. Management Science, 37(5), 519-531. doi:10.1287/mnsc.37.5.519Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7), 1443-1471. doi:10.1016/s0378-4266(02)00271-6Mansini, R. (2003). LP solvable models for portfolio optimization: a classification and computational comparison. IMA Journal of Management Mathematics, 14(3), 187-220. doi:10.1093/imaman/14.3.187Hirschberger, M., Steuer, R. E., Utz, S., Wimmer, M., & Qi, Y. (2013). Computing the Nondominated Surface in Tri-Criterion Portfolio Selection. Operations Research, 61(1), 169-183. doi:10.1287/opre.1120.1140Utz, S., Wimmer, M., Hirschberger, M., & Steuer, R. E. (2014). Tri-criterion inverse portfolio optimization with application to socially responsible mutual funds. European Journal of Operational Research, 234(2), 491-498. doi:10.1016/j.ejor.2013.07.024Utz, S., Wimmer, M., & Steuer, R. E. (2015). Tri-criterion modeling for constructing more-sustainable mutual funds. European Journal of Operational Research, 246(1), 331-338. doi:10.1016/j.ejor.2015.04.035Chang, T.-J., Meade, N., Beasley, J. E., & Sharaiha, Y. M. (2000). Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research, 27(13), 1271-1302. doi:10.1016/s0305-0548(99)00074-xMaringer, D., & Kellerer, H. (2003). Optimization of cardinality constrained portfolios with a hybrid local search algorithm. OR Spectrum, 25(4), 481-495. doi:10.1007/s00291-003-0139-1Shaw, D. X., Liu, S., & Kopman, L. (2008). Lagrangian relaxation procedure for cardinality-constrained portfolio optimization. Optimization Methods and Software, 23(3), 411-420. doi:10.1080/10556780701722542Soleimani, H., Golmakani, H. R., & Salimi, M. H. (2009). Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm. Expert Systems with Applications, 36(3), 5058-5063. doi:10.1016/j.eswa.2008.06.007Anagnostopoulos, K. P., & Mamanis, G. (2011). The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms. Expert Systems with Applications. doi:10.1016/j.eswa.2011.04.233Woodside-Oriakhi, M., Lucas, C., & Beasley, J. E. (2011). Heuristic algorithms for the cardinality constrained efficient frontier. European Journal of Operational Research, 213(3), 538-550. doi:10.1016/j.ejor.2011.03.030Meghwani, S. S., & Thakur, M. (2017). Multi-criteria algorithms for portfolio optimization under practical constraints. Swarm and Evolutionary Computation, 37, 104-125. doi:10.1016/j.swevo.2017.06.005Liagkouras, K., & Metaxiotis, K. (2016). A new efficiently encoded multiobjective algorithm for the solution of the cardinality constrained portfolio optimization problem. Annals of Operations Research, 267(1-2), 281-319. doi:10.1007/s10479-016-2377-zMetaxiotis, K., & Liagkouras, K. (2012). Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review. Expert Systems with Applications, 39(14), 11685-11698. doi:10.1016/j.eswa.2012.04.053Silva, Y. L. T. V., Herthel, A. B., & Subramanian, A. (2019). A multi-objective evolutionary algorithm for a class of mean-variance portfolio selection problems. Expert Systems with Applications, 133, 225-241. doi:10.1016/j.eswa.2019.05.018Chang, T.-J., Yang, S.-C., & Chang, K.-J. (2009). Portfolio optimization problems in different risk measures using genetic algorithm. Expert Systems with Applications, 36(7), 10529-10537. doi:10.1016/j.eswa.2009.02.062Liagkouras, K. (2019). A new three-dimensional encoding multiobjective evolutionary algorithm with application to the portfolio optimization problem. Knowledge-Based Systems, 163, 186-203. doi:10.1016/j.knosys.2018.08.025Kaucic, M., Moradi, M., & Mirzazadeh, M. (2019). Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures. Financial Innovation, 5(1). doi:10.1186/s40854-019-0140-6Babaei, S., Sepehri, M. M., & Babaei, E. (2015). Multi-objective portfolio optimization considering the dependence structure of asset returns. European Journal of Operational Research, 244(2), 525-539. doi:10.1016/j.ejor.2015.01.025Ruiz, A. B., Saborido, R., Bermúdez, J. D., Luque, M., & Vercher, E. (2019). Preference-based evolutionary multi-objective optimization for portfolio selection: a new credibilistic model under investor preferences. Journal of Global Optimization, 76(2), 295-315. doi:10.1007/s10898-019-00782-1Anagnostopoulos, K. P., & Mamanis, G. (2010). 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Proactive tutoring as a factor in improving the results of online training

En el presente trabajo de investigación se plantea como hipótesis que un modelo de tutorización proactiva, centrado en el acompañamiento y seguimiento continuo del estudiante online, mejora la tasa de aprobados y la satisfacción de los estudiantes. Para validar dicha hipótesis se describen y comparan los resultados de las encuestas de satisfacción y la tasa de aprobados de los cursos impartidos a través del Plan de Estudios Coordinados en Asesoría Financiera del Centro de Formación Permanente de la Universitat Politècnica de València, incluyéndose en el estudio 27 cursos que siguen un modelo de tutorización proactiva con un total de 5.613 estudiantes matriculados y una muestra de 2.500 cuestionarios y 30 cursos que siguen un modelo de tutorización reactiva con un total de 1.862 estudiantes matriculados y una muestra de 583 cuestionarios. Los resultados obtenidos permiten aceptar la hipótesis de partida confirmando que en los cursos que se ha seguido un modelo de tutorización proactiva la tasa de aprobados es un 30% más alta, y al respecto de la satisfacción del alumnado, en ambos tipos de tutorización, el patrón general de respuestas a las encuestas, con ciertas diferencias en la distribución de las mismas, es similar si bien, la satisfacción es más alta en los cursos que han seguido un modelo de tutorización proactivo, mientras que la insatisfacción es mayor en los de tutorización reactiva

Evaluating ESG corporate performance using a new neutrosophic AHP-TOPSIS based approach

Corporate sustainability reports’ credibility of environmental, social, and governance (ESG) information has received a significant focus of attention in the businesses landscape. Over the last years, various methodologies and multicriteria approaches have been developed to assess the ESG performance of companies. To consider the uncertainty that arises from imprecision and subjectivity in evaluating ESG criteria, this paper proposes to develop a novel hybrid methodology that combines AHP and TOPSIS techniques under a neutrosophic environment. We test the suggested proposal through a real case study of the leading companies in the oil and gas industry. Moreover, we conduct a sensitivity analysis for evaluating any discrepancies in the ranking due to using different fuzzy numbers and weighting vectors. First published online 05 July 202

Las TIC en la formación online

Las TIC han tomado un papel importantísimo en nuestra sociedad y se utilizan en multitud de actividades. Permiten un fácil acceso a la información en cualquier formato y de manera fácil y rápida. Las TIC ayudan a mejorar la actividad pedagógica, pero es clave estar bien preparado. La informática ayuda a mejorar tanto la docencia como la competencia personal frente al ordenador. Las funciones principales de un profesor en la impartición de un curso online son las de organizador y facilitador de la participación de los estudiantes. El éxito del curso, en gran parte, está en el compromiso, el entusiasmo y la dedicación intelectual que ponga el profesor en la dinámica diaria. Las modalidades de formación online pueden ser e-learning y b-learning y en función de los escenarios de docencia online se han de elegir un tipo u otro de herramientas