Alejandro Peña

Adaptive and intelligent web based education system: Towards an integral architecture and framework

In this paper it is presented our contribution for carrying out adaptive and intelligent Web-based Education Systems (WBES) that take into account the individual student learning requirements, by means of a holistic architecture and Framework for developing WBES. In addition, three basic modules of the proposed WBES are outlined: an Authoring tool, a Semantic Web-based Evaluation, and a Cognitive Maps-based Student Model. As well, it is stated a Service Oriented Architecture (SOA) oriented to deploy reusable, accessible, durable and interoperable services. The approach enhances the Learning Technology Standard Architecture, proposed by IEEE-LTSA (Learning Technology System Architecture) [IEEE 1484.1/D9 LTSA (2001). Draft standard for learning technology - learning technology systems architecture (LTSA). New York, USA. URL: http://ieee.ltsc.org/wg1], and the Sharable Content Object Reusable Model (SCORM), claimed by Advanced Distributed Learning (ADL) [Advanced Distributed Learning Initiative (2004). URL: http://www.adlnet.org].

Review: Causal knowledge and reasoning by cognitive maps: Pursuing a holistic approach

Due to the lack of an integral study about cognitive maps (CM) that focus on the causal phenomenon, this paper introduces the underlying concepts towards a holistic conceptual model, enhanced by a profile of several versions. We illustrate the use of CM through their application into the Web-based Education Systems (WBES). From the causal perspective, CM depict and simulate the systems dynamics based upon qualitative knowledge about a specific domain. A CM is a visual digraph that identifies the concepts of a given subject of analysis. CM show causal-effect relationships among the concepts and outline complex structures. This tool aims to predict the evolution of a model through causal inference. This kind of inference estimates the degree of significance of change of the concepts in the context of the whole system. The behavior of a CM is given away during iterations that update the variation of the concept state values until reach a stable point in a search space, a pattern of states or a chaotic region. The purpose of this research is to share its findings, depict the work done and promote the use of CM in a broad spectrum of domains.

Collaborative student modeling by cognitive maps

The evolution of the computer supported collaborative learning (CSCL) implies the definition and managing of a student model (SM) regarding the collaborative group of learners capable to represent the cognitive state achieved in the knowledge domain acquired from the learner experiences. This model is composed by two levels, one oriented to represent the SM of each learner, and the other to recreate the SM of the team that have been learning by an a collaborative strategy. In order to accomplish this challenge, the proposal depicts cognitive maps (CM) to represent the concepts and their relationships that are involved in the considerations of the learners and the whole team. With this approach, it is possible to deal with multiples perspectives and try to ensemble them in an integral view, developing the levels of student modeling in a collaborative fashion.

Knowledge and reasoning supported by cognitive maps

A powerful and useful approach for modeling knowledge and qualitative reasoning is the Cognitive Map. The background of Cognitive Maps is the research about learning environments carried out by Cognitive Psychology since the nineteenth century. Along the last thirty years, these underlying findings inspired the development of computational models to deal with causal phenomena. So, a Cognitive Map is a structure of concepts of a specific domain that are related through cause-effect relations with the aim to simulate behavior of dynamic systems. In spite of the short life of the causal Cognitive Maps, nowadays there are several branches of development that focus on qualitative, fuzzy and uncertain issues. With this platform wide spectra of applications have been developing in fields like game theory, information analysis and management sciences. Wherefore, with the purpose to promote the use of this kind of tool, in this work is surveyed three branches of Cognitive Maps; and it is outlined one application of the Cognitive Maps for the student modeling that shows a conceptual design of a project in progress.

An Integrated Inverse Adaptive Neural Fuzzy System with Monte-Carlo Sampling Method for Operational Risk Management

Abstract Operational risk refers to deficiencies in processes, systems, people or external events, which may generate losses for an organization. The Basel Committee on Banking Supervision has defined different possibilities for the measurement of operational risk, although financial institutions are allowed to develop their own models to quantify operational risk. The advanced measurement approach, which is a risk-sensitive method for measuring operational risk, is the financial institutions preferred approach, among the available ones, in the expectation of having to hold less regulatory capital for covering operational risk with this approach than with alternative approaches. The advanced measurement approach includes the loss distribution approach as one way to assess operational risk. The loss distribution approach models loss distributions for business-line-risk combinations, with the regulatory capital being calculated as the 99,9% operational value at risk, a percentile of the distribution for the next year annual loss. One of the most important issues when estimating operational value at risk is related to the structure (type of distribution) and shape (long tail) of the loss distribution. The estimation of the loss distribution, in many cases, does not allow to integrate risk management and the evolution of risk; consequently, the assessment of the effects of risk impact management on loss distribution can take a long time. For this reason, this paper proposes a flexible integrated inverse adaptive fuzzy inference model, which is characterized by a Monte-Carlo behavior, that integrates the estimation of loss distribution and different risk profiles. This new model allows to see how the management of risk of an organization can evolve over time and it effects on the loss distribution used to estimate the operational value at risk. The experimental study results, reported in this paper, show the flexibility of the model in identifying (1) the structure and shape of the fuzzy input sets that represent the frequency and severity of risk; and (2) the risk profile of an organization. Therefore, the proposed model allows organizations or financial entities to assess the evolution of their risk impact management and its effect on loss distribution and operational value at risk in real time.

Predictive Causal Approach for Student Modeling

This work proposes a Student Model (SM) oriented to predict causal effects that teaching and learning experiences produce on a student before their delivery. Our student modeling approach elicits concepts from domains that depict the educative program and the individual profile of the student, as content description and cognitive skills. The Cognitive Map sketches causal-effect relationships among the concepts involved by means of Fuzzy Rule Bases. Concepts and relations are fully described in an ontology. Based on the ontology, it is outcome a Cognitive Map for each option of teaching-learning experience. The analysis of the model depicted by the Cognitive Map is done through its activation. This process is a kind of simulation, which traces fuzzy causal inferences in order to estimate behaviors and final states for the concepts. The prediction of the causal results is achieved according to the interpretation of the evolution and final values of the concepts. So in Web-based Education Systems (WBES) that own several options for content, sequencing, and evaluation material, our student modeling offers a predictive support for student-centered education.

A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events

Abstract Operational Risk (OpR) refers to the possibility of suffering losses resulting from inadequate or failure of processes and/or technology, inadequate behaviour of people or external events. OpR was one of the main risks that led to the 2008 global financial crisis. Limitations of the analytical models that are applied in estimating this risk surface when qualitative information, frequently associated with OpR events, is used. To determine the magnitude of OpR in financial organisations, qualitative data and also historical data from risk events can be used. Current research trends that focus on the development of analytical models, by using different databases, to estimate the Operational Value at Risk (OpVaR) still lack models based on qualitative information, risk management profiles and the ability to integrate different databases of OpR events. In this paper we present a fuzzy model to estimate the OpVaR of an organisation by working with two different databases that contain internal available data and external or observed data. The proposed model considers: (1) the intrinsic properties of the data as fuzzy sets related to the linguistic variables of the observed data (external) and the data from available databases (internal), and (2) a series of management profiles to mitigate the effect that external data usually causes in estimating the OpVaR of an organisation. The results obtained with the proposed model allow an organisation to estimate and determine the behaviour of the OpVaR over time by using different risk profiles. The integration of qualitative information, different risk profiles (ranging from weak to strong risk management), and internal and external databases contributes to the advancement of estimating the OpVaR in risk management .

Methodological proposal of financial modelling using dynamic scenarios from multivariable data tables

This paper aims at proposing a methodological strategy for teaching the modelling of dynamic scenarios from Multivariable Data Tables using Microsoft Excel as a technological tool. It provides a step-by-step guide for improving the teaching-learning method on the development of financial models, which could also be used in other areas. The methodology allows identifying the most important aspects of developing financial models so students-professors could be guided towards a more pedagogical approach promoting the incorporation of technological tools in the classroom. This work plays a pivotal role in financial modelling teaching since it provides relevant elements leading to the development of systematic thinking among university students based on cognitive activities. Also, various contributions to research on the teaching-learning process in modelling are discussed. For validating the proposal, an airports financial valuation was used as a case study. As a result, this paper contributes to increasing the role of computational tools and students-professors involvement in the development of systemic thinking and cognitive skills.

Flexible inverse adaptive fuzzy inference model to identify the evolution of Operational Value at Risk for improving operational risk management

Abstract Operational risk was one of the most important risks in the 2008 global financial crisis. This is due to limitations of the applied models in explaining and estimating this type of risk from highly qualitative information related to failures in the operations of financial organizations. A review of research literature on this area indicates an increase in the development of models for the estimation of the operational value at risk. However, there is a lack of models that use qualitative information for estimating this type of risk. Motivated by this finding, we propose a Flexible Inverse Adaptive Fuzzy Inference Model that integrates both a novel Montecarlo sampling method for the linguistic input variables of frequency and severity that allow the characterization of a risk event, the impact of risk management matrices to estimate the loss distribution and the associated operational value at risk. The methodology follows a loss distribution approach as defined by Basel II. A benefit of the proposed model is that it works with highly qualitative risk data and it also connects the risk measurement (operational value at risk) with risk management, based on risk management matrices. This way, we mitigate limitations related to a lack of available operational risk event data when assessing operational risk. We evaluate the experimental results obtained through the proposed model by using the Index of Agreement indicator. The results provide a flexible loss distribution under different risk profiles or risk management matrices that explain the evolution of operational risk in real time.