Aplicando la metodología flipped-teaching en el Grado de Ingeniería Informática: una experiencia práctica

En el Grado de Ingeniería Informática de la Universitat Politècnica de València se llevó a cabo durante el curso 2014-15 una experiencia piloto de aplicación de la metodología Flipped-Teaching en todas las asigna-turas obligatorias de segundo curso del grado. La metodología Flipped-Teaching (o clase inversa) consiste en invertir el modelo tradicional de docencia, de modo que la lección magistral habitual de aula se sustituye por un conjunto de materiales en línea (vídeos, lecturas, etc.) que el alumno debe revisar previa a su asistencia a clase. Por su parte, las sesiones de aula se transforman en sesiones fundamental-mente prácticas, con actividades individuales o en grupo, pensadas principalmente para la resolución de ejercicios y problemas, la aclaración de dudas y la discusión sobre aspectos relevantes. En este trabajo se presenta la organización de la docencia, los métodos utilizados, así como la evaluación de la experiencia y los resultados obtenidos para una de las asignaturas del Grado de Ingeniería Informática en las que se aplicó esta metodología, en concreto "Concurrencia y Sistemas Distribuidos". La metodología Flipped-Teaching nos ha permitido aumentar la motivación y participación de los estudiantes así como mejorar su proceso de autoaprendizaje. La motivación de los alumnos ha sido enorme, reflejándose claramente tanto en su participación activa en la clase como por los buenos resultados de evaluación obtenidos

Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks

The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic evaluation of complete professional argumentative debates. In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.</p

Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks

The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic evaluation of complete professional argumentative debates. In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.</p

Empowering users regarding the sensitivity of their data in social networks through nudge mechanisms

The use of online social networks (OSNs) is a continuous trade-off between relinquishing some privacy in exchange for getting some social benefits like maintaining (or creating new) relationships, getting support, influencing others’ opinions, etc. OSN users are faced with this decision each time they share information. The amount of information or its sensitivity is directly related to the amount of users’ loss of privacy. Currently, there are several approaches for assessing the sensitivity of the information based on the willingness of users to provide them, the monetary benefits derived from extracting knowledge of them, the amount of information they provide, etc. In this work, we focus on quantifying data sensitivity as the combination of all of the approaches and adapting them to the OSN domain. Furthermore, we propose a way of scoring publication sensitivity as the accumulative value of the sensitivity of the information types included in it. Finally, an experiment with 196 teenagers was carried out to assess the effectiveness of empowering users regarding the sensitivity of the publication. The results show a significant effect on users’ privacy behavior by the nudge message and the sensitivity included in it

Using a Case-Based Reasoning Approach for Trading in Sports Betting Markets

The sports betting market has emerged as one of the most lucrative markets in recent years. Trading in sports betting markets entails predicting odd movements in order to bet on an outcome, whilst also betting on the opposite outcome, at different odds in order to make a profit, regardless of the final result. These markets are mainly composed by humans, which take decisions according to their past experience in these markets. However, human rational reasoning is limited when taking quick decisions, being influenced by emotional factors and offering limited calibration capabilities for estimating probabilities. In this paper, we show how artificial techniques could be applied to this field and demonstrate that they can outperform even the bevahior of high-experienced humans. To achieve this goal, we propose a case-based reasoning model for trading in sports betting markets, which is integrated in an agent to provide it with the capabilities to take trading decisions based on future odd predictions. In order to test the performance of the system, we compare trading decisions taken by the agent with trading decisions taken by human traders when they compete in real sports betting markets.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and project TIN2011-27652-C03-01. Juan M. Alberola has received a grant from Ministerio de Ciencia e Innovacion de Espana (AP2007-00289).Alberola Oltra, JM.; García Fornes, AM. (2013). Using a Case-Based Reasoning Approach for Trading in Sports Betting Markets. Applied Intelligence. 38(3):465-477. https://doi.org/10.1007/s10489-012-0381-9S465477383Aamodt A (1990) Knowledge-intensive case-based reasoning and sustained learning. In: Topics in case-based reasoning. Springer, Berlin, pp 274–288Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Ahn JJ, Byun HW, Oh KJ, Kim TY (2012) Bayesian forecaster using class-based optimization. Appl Intell 36(3):553–563Alberola JM, Garcia-Fornes A, Espinosa A (2010) Price prediction in sports betting markets. In: Proceedings of the 8th German conference on multiagent system technologies, pp 197–208Arias-Aranda D, Castro JL, Navarro M, Zurita JM (2009) A cbr system for knowing the relationship between flexibility and operations strategy. In: Proceedings of the 18th international symposium on foundations of intelligent systems, ISMIS’09, pp 463–472Ates C (2004) Prediction markets are only human: subadditivity in probability judgments. In: MSC in finance and international businessBerlemann M, Schmidt C (2001) Predictive accuracy of political stock markets—empirical evidence from a European perspective. Technical report 2001-57Betfair (2009) http://www.betfaircorporate.comChen Y, Goel S, Pennock D (2008) Pricing combinatorial markets for tournaments. In: STOC’08: proceedings of the 40th annual ACM symposium on theory of computing. ACM Press, New York, pp 305–314Debnath S, Pennock DM, Giles CL, Lawrence S (2003) Information incorporation in online in-game sports betting markets. In: Proceedings of the 4th ACM conference on electronic commerce, EC ’03. ACM Press, New York, pp 258–259. doi: 10.1145/779928.779987Fischoff B, Slovic P, Lichtenstein S (1977) Knowing with certainty: the appropriateness of extreme confidence. J Exp Psychol Human Percept Perform 3:552–564Forsythe R, Rietz T, Ross T (1999) Wishes, expectations and actions: a survey on price formation in election stock markets. J Econ Behav Organ 39(1):83–110Fortnow L, Kilian J, Pennock DM, Wellman MP (2005) Betting Boolean-style: a framework for trading in securities based on logical formulas. Decis Support Syst 39(1):87–104. doi: 10.1016/j.dss.2004.08.010Gayer G (2010) Perception of probabilities in situations of risk: a case based approach. Games Econ Behav 68(1):130–143Guo M, Pennock D (2009) Combinatorial prediction markets for event hierarchies. In: Proc of the 8th AAMAS’09. Int foundation for autonomous agents and multiagent systems, pp 201–208Huang W, Lai K, Nakamori Y, Wang S (2004) Forecasting foreign exchange rates with artificial neural networks: a review. Int J Inf Technol Decis Mak 3(1):145–165Hüllermeier E (2007) Case-based approximate reasoning. Theory and decision library, vol 44. Springer, BerlinKim K-J, Ahn H (2012) Simultaneous optimization of artificial neural networks for financial forecasting. Appl Intell 36(4):887–898LeBaron B (1998) Agent based computational finance: suggested readings and early research. J Econ Dyn ControlLiu Y, Yang C, Yang Y, Lin F, Du X, Ito T (2012) Case learning for cbr-based collision avoidance systems. Appl Intell 36(2):308–319Love BC (2008) Behavioural finance and sports betting markets. In: MSC in finance and international businessLuque C, Valls JM, Isasi P (2011) Time series prediction evolving Voronoi regions. Appl Intell 34(1):116–126Mantaras RLD, McSherry D, Bridge D, Leake D, Smyth B, Craw S, Faltings B, Maher M, Lou C, Forbus MCK, Keane M, Aamodt A, Watson I (2005) Retrieval, reuse, revision and retention in case-based reasoning. Knowl Eng Rev 20(3):215–240Moody J (1995) Economic forecasting: challenges and neural network solutions. In: Proceedings of the international symposium on artificial neural networksOntañón S, Plaza E (2009) Argumentation-based information exchange in prediction markets. Argument Multi-Agent Syst 5384:181–196Ontañón S, Plaza E (2011) An argumentation framework for learning, information exchange, and joint-deliberation in multi-agent systems. Multiagent Grid Syst 7:95–108Palmer R, Arthur W, Holland J, Lebaron B, Tayler P (1994) Artificial economic life: a simple model of a stock market. Physica D 75:264–274Pennock D, Debnath S, Glover E, Giles C (2002) Modelling information incorporation in markets, with application to detecting and explaining events. In: Proceedings of the 18th annual conference on uncertainty in artificial intelligence (UAI-02), San Francisco, CA. Morgan Kaufmann, San Mateo, pp 404–405Pennock DM, Lawrence S, Nielsen FÅ, Giles CL (2001) Extracting collective probabilistic forecasts from web games. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’01. ACM Press, New York, pp 174–183. doi: 10.1145/502512.502537Plott CR (2000) Markets as information gathering tools. South Econ J 67(1):2–15Qian B, Rasheed K (2007) Stock market prediction with multiple classifiers. Appl Intell 26(1):25–33Raudys S, Zliobaite I (2006) The multi-agent system for prediction of financial time series. In: ICAISC, vol 4029. Springer, Berlin, pp 653–662Schmidt C, Werwatz A (2002) How accurate do markets predict the outcome of an event? The euro 2000 soccer championship experiment, 2002-09. Max Planck Institute of Economics, Strategic Interaction Group. http://ideas.repec.org/p/esi/discus/2002-09.htmlShiu SCK, Pal SK (2004) Case-based reasoning: concepts, features and soft computing. Appl Intell 21(3):233–238Wellman MP, Reeves DM, Lochner KM, Vorobeychik Y (2004) Price prediction in a trading agent competition. J Artif Intell Res 21:19–3

Persuasion-enhanced computational argumentative reasoning through argumentation-based persuasive frameworks

One of the greatest challenges of computational argumentation research consists of creating persuasive strategies that can effectively influence the behaviour of a human user. From the human perspective, argumentation represents one of the most effective ways to reason and to persuade other parties. Furthermore, it is very common that humans adapt their discourse depending on the audience in order to be more persuasive. Thus, it is of utmost importance to take into account user modelling features for personalising the interactions with human users. Through computational argumentation, we can not only devise the optimal solution, but also provide the rationale for it. However, synergies between computational argumentative reasoning and computational persuasion have not been researched in depth. In this paper, we propose a new formal framework aimed at improving the persuasiveness of arguments resulting from the computational argumentative reasoning process. For that purpose, our approach relies on an underlying abstract argumentation framework to implement this reasoning and extends it with persuasive features. Thus, we combine a set of user modelling and linguistic features through the use of a persuasive function in order to instantiate abstract arguments following a user-specific persuasive policy. From the results observed in our experiments, we can conclude that the framework proposed in this work improves the persuasiveness of argument-based computational systems. Furthermore, we have also been able to determine that human users place a high level of trust in decision support systems when they are persuaded using arguments and when the reasons behind the suggestion to modify their behaviour are provided

VMFS: herramienta visual para la enseñanza del funcionamiento de un sistema de ficheros

Se ha desarrollado una herramienta que permite a los alumnos conocer de forma sencilla las distintas partes de que consta un sistema de ficheros en el sistema operativo MINIX. En concreto, mediante VMFS1 es posible estudiar cómo se gestiona en MINIX la asignación del espacio en disco a ficheros, así como las distintas estructuras de datos que se emplean en dicha gestión para implementar diferentes tipos de ficheros. Además, al tratarse de una intuitiva aplicación gráfica, resulta una herramienta muy adecuada para realizar prácticas en el marco de una asignatura orientada a la enseñanza de conceptos básicos y técnicas fundamentales de los sistemas operativos, cuyos alumnos normalmente carecen de grandes conocimientos de programación

Persuasion-enhanced computational argumentative reasoning through argumentation-based persuasive frameworks

One of the greatest challenges of computational argumentation research consists of creating persuasive strategies that can effectively influence the behaviour of a human user. From the human perspective, argumentation represents one of the most effective ways to reason and to persuade other parties. Furthermore, it is very common that humans adapt their discourse depending on the audience in order to be more persuasive. Thus, it is of utmost importance to take into account user modelling features for personalising the interactions with human users. Through computational argumentation, we can not only devise the optimal solution, but also provide the rationale for it. However, synergies between computational argumentative reasoning and computational persuasion have not been researched in depth. In this paper, we propose a new formal framework aimed at improving the persuasiveness of arguments resulting from the computational argumentative reasoning process. For that purpose, our approach relies on an underlying abstract argumentation framework to implement this reasoning and extends it with persuasive features. Thus, we combine a set of user modelling and linguistic features through the use of a persuasive function in order to instantiate abstract arguments following a user-specific persuasive policy. From the results observed in our experiments, we can conclude that the framework proposed in this work improves the persuasiveness of argument-based computational systems. Furthermore, we have also been able to determine that human users place a high level of trust in decision support systems when they are persuaded using arguments and when the reasons behind the suggestion to modify their behaviour are provided

Desarrollo de prototipos hardware para una maqueta de tren con fines docentes

El uso de elementos reales con fines docentes es cada vez más frecuente. El presente trabajo presenta la experiencia de la puesta en marcha de una maqueta de trenes describiendo los problemas surgidos a la hora de realizar el control por computador de la misma, así como las soluciones propuestas. El trabajo se centra en la descripción de la infraestructura hardware desarrollada sobre una maqueta comercial, para permitir el control individual de los elementos móviles (trenes, cambios de vía…) mediante un ordenador

Feedback Efectivo en Prácticas de Programación

Las asignaturas de carácter práctico como la programación, presentan históricamente un alto índice de abandonos y unas tastas de aprobados bajas. Una característica de estas asignaturas es que el material que se aprende, necesita ser afianzado para aprender nuevos conceptos, por lo tanto, un feedback progresivo y continuo es esencial para la motivación de los alumnos. En este artículo, presentamos una experiencia docente que obtiene dicho feedback mediante el uso de la plataforma educativa. El impacto a diferentes niveles de esta experiencia es analizado en un grupo de alumnos.Alberola Oltra, JM.; García Fornes, AM. (2013). Feedback Efectivo en Prácticas de Programación. VAEP-RITA. Versión Abierta Español-Portugués. 1(2):88-96. http://hdl.handle.net/10251/60536S88961