Agustín Gutiérrez

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.

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.

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.