A soft computing decision support framework to improve the e-learning experience

In this paper an e-learning decision support framework based on a set of soft computing techniques is presented. The framework is mainly based on the FIR methodology and two of its key extensions: a set of Causal Relevance approaches (CR-FIR), that allow to reduce uncertainty during the forecast stage; and a Rule Extraction algorithm (LR-FIR), that extracts comprehensible, actionable and consistent sets of rules describing the student learning behavior. The data set analyzed was gathered from the data generated from user’s interaction with an e-learning environment. The introductory course data set was analyzed with the proposed framework with the goal to help virtual teachers to understand the underlying relations between the actions of the learners, and make more interpretable the student learning behavior. The results obtained improve system understanding and provide valuable knowledge to teachers about the course performance.Postprint (author’s final draft

Un algoritmo para la extracción automática de reglas lógicas a partir de modelos FIR

In this report the LR-FIR (Logical Rules with FIR) algorithm is described. The main goal of LR-FIR is to extract, in an automatic way, a set of logical rules that explain system’s behaviour. The algorithm starts from the model identified by the Fuzzy Inductive Reasoning (FIR) methodology and obtains a compacted set of logical rules. A FIR model is composed of the mask, that represents system’s structure, and the pattern rule base, that contains system’s behaviour. This report is organized in two sections. The first one presents FIR methodology in detail, whereas the second one describes the LR-FIR algorithm developed in an accurate way. En este reporte se describe el algoritmo LR-FIR (Logical Rules with FIR), que tiene como objetivo extraer de manera automática un conjunto de reglas lógicas que expliquen el comportamiento del sistema. LR-FIR parte del modelo del sistema identificado mediante la metodología del Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés). Un modelo FIR está compuesto de la máscara que describe la estructura del sistema y la base de reglas patrón que aglutina el comportamiento de éste. Este reporte está organizado en dos secciones. En la primera de ellas se presenta en la metodología FIR mientas que en la segunda se describe en detalle el algoritmo LR-FIR desarrollado

Un algoritmo para la extracción automática de reglas lógicas a partir de modelos FIR

In this report the LR-FIR (Logical Rules with FIR) algorithm is described. The main goal of LR-FIR is to extract, in an automatic way, a set of logical rules that explain system’s behaviour. The algorithm starts from the model identified by the Fuzzy Inductive Reasoning (FIR) methodology and obtains a compacted set of logical rules. A FIR model is composed of the mask, that represents system’s structure, and the pattern rule base, that contains system’s behaviour. This report is organized in two sections. The first one presents FIR methodology in detail, whereas the second one describes the LR-FIR algorithm developed in an accurate way. En este reporte se describe el algoritmo LR-FIR (Logical Rules with FIR), que tiene como objetivo extraer de manera automática un conjunto de reglas lógicas que expliquen el comportamiento del sistema. LR-FIR parte del modelo del sistema identificado mediante la metodología del Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés). Un modelo FIR está compuesto de la máscara que describe la estructura del sistema y la base de reglas patrón que aglutina el comportamiento de éste. Este reporte está organizado en dos secciones. En la primera de ellas se presenta en la metodología FIR mientas que en la segunda se describe en detalle el algoritmo LR-FIR desarrollado

An e-Learning toolbox based on rule-based fuzzy approaches

In this paper, an e-Learning toolbox based on a set of fuzzy logic data mining techniques is presented. The toolbox is mainly based on the fuzzy inductive reasoning (FIR) methodology and two of its key extensions: (i) the linguistic rules extraction algorithm (LR-FIR), which extracts comprehensible and consistent sets of rules describing students’ learning behavior, and (ii) the causal relevance approach (CR-FIR), which allows to reduce uncertainty during a student’s performance prediction stage, and provides a relative weighting of the features involved in the evaluation process. In addition, the presented toolbox enables, in an incremental way, detecting and grouping students with respect to their learning behavior, with the main goal to timely detect failing students, and properly provide them with suitable and actionable feedback. The proposed toolbox has been applied to two different datasets gathered from two courses at the Latin American Institute for Educational Communication virtual campus. The introductory and didactic planning courses were analyzed using the proposed toolbox. The results obtained by the functionalities offered by the platform allow teachers to make decisions and carry out improvement actions in the current course, i.e., to monitor specific student clusters, to analyze possible changes in the different evaluable activities, or to reduce (to some extent) teacher workload.Peer ReviewedPostprint (published version

A soft computing decision support framework to improve the e-learning experience

In this paper an e-learning decision support framework based on a set of soft computing techniques is presented. The framework is mainly based on the FIR methodology and two of its key extensions: a set of Causal Relevance approaches (CR-FIR), that allow to reduce uncertainty during the forecast stage; and a Rule Extraction algorithm (LR-FIR), that extracts comprehensible, actionable and consistent sets of rules describing the student learning behavior. The data set analyzed was gathered from the data generated from user’s interaction with an e-learning environment. The introductory course data set was analyzed with the proposed framework with the goal to help virtual teachers to understand the underlying relations between the actions of the learners, and make more interpretable the student learning behavior. The results obtained improve system understanding and provide valuable knowledge to teachers about the course performance

Written Documents Analyzed as Nature-Inspired Processes: Persistence, Anti-Persistence, and Random Walks—We Remember, as Along Came Writing—T. Holopainen

Written communication is pivotal for societies to develop. However, lexicon and depth of information vary greatly among texts according to their purpose. Scientific texts, diffusion of science reports, general and area-specific news are all written differently. Thus, we explore the characterization of different text categories through a nature-inspired feature known as the Hurst parameter. We contend that the Hurst exponent is useful to unveil the rhetorical structure within written documents. We collected and processed texts in five categories: scientific articles, diffusion of science reports, business news, entertainment news, and random texts. Each category contains 350 documents. We found that the median for scientific texts has the highest value of the Hurst parameter (0.575), followed by business news (0.54); the median for randomly-generated texts is 0.48, which lies in the region associated with random walks. The median value for diffusion texts is 0.49, and for entertainment texts is 0.53. However, these two categories present high dispersion. We conclude that the Hurst parameter is a measure that quantifies the structure of communication in the selected categories of texts. Application of our finding in the field of e-research is discussed

On quasi-interpolation by radial basis functions with scattered centers

SIGLEAvailable from British Library Document Supply Centre- DSC:9106.1605(CU-DAMTP-NA--6/1992) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Rule-based assistance to brain tumour diagnosis using LR-FIR

This paper describes a process of rule-extraction from a multi-centre brain tumour database consisting of nuclear magnetic res- onance spectroscopic signals. The expert diagnosis of human brain tumours can benefit from computer-aided assistance, which has to be readily interpretable by clinicians. Interpretation can be achieved through rule extraction, which is here performed using the LR-FIR algorithm, a method based on fuzzy logic. The experimental results of the classification of three groups of tumours indicate in this study that just three spectral frequencies, out of the 195 from a range pre-selected by experts, are enough to represent, in a simple and intuitive manner, most of the knowledge required to discriminate these groups.Postprint (published version

Rule-based assistance to brain tumour diagnosis using LR-FIR

This paper describes a process of rule-extraction from a multi-centre brain tumour database consisting of nuclear magnetic res- onance spectroscopic signals. The expert diagnosis of human brain tumours can benefit from computer-aided assistance, which has to be readily interpretable by clinicians. Interpretation can be achieved through rule extraction, which is here performed using the LR-FIR algorithm, a method based on fuzzy logic. The experimental results of the classification of three groups of tumours indicate in this study that just three spectral frequencies, out of the 195 from a range pre-selected by experts, are enough to represent, in a simple and intuitive manner, most of the knowledge required to discriminate these groups

Rule-based assistance to brain tumour diagnosis using LR-FIR

This paper describes a process of rule-extraction from a multi-centre brain tumour database consisting of nuclear magnetic res- onance spectroscopic signals. The expert diagnosis of human brain tumours can benefit from computer-aided assistance, which has to be readily interpretable by clinicians. Interpretation can be achieved through rule extraction, which is here performed using the LR-FIR algorithm, a method based on fuzzy logic. The experimental results of the classiffication of three groups of tumours indicate in this study that just three spectral frequencies, out of the 195 from a range pre-selected by experts, are enough to represent, in a simple and intuitive manner, most of the knowledge required to discriminate these groups