The Influence of Gender, Intellectual Ability, Academic Self-Concept, Self-Regulation, Learning Strategies, Popularity and Parent Involvement in Early Adolescence

The present study examined the predictive effects of gender, intellectual ability, self-concept, motivation, learning strategies, popularity and parent involvement on academic achievement. Hiearchical regression analysis were performed with six steps in which each variable was included, among a sample of 1398 high school students (mean age = 12.5; standard deviation = .67) of eight education centers from the province of Alicante (Spain). The results revealed significant predictive effects of all of the variables, explaining 59.1% of the total variance.

SARASOM: a supervised architecture based on the recurrent associative SOM

We present and evaluate a novel supervised recurrent neural network architecture, the SARASOM, based on the associative self-organizing map. The performance of the SARASOM is evaluated and compared with the Elman network as well as with a hidden Markov model (HMM) in a number of prediction tasks using sequences of letters, including some experiments with a reduced lexicon of 15 words. The results were very encouraging with the SARASOM learning better and performing with better accuracy than both the Elman network and the HMM.

An exercises editor for an e-learning environment

One of the most important tenets of e-learning is that it bridges work and learning. A great e-learning experience brings learning into the work environment. This is a key point, the capacity to construct a work environment when the student can develop proper tasks to complete the learning process. This paper describes a work environment based on the development of two tools, an exercises editor and an exercises viewer. Both tools are able to manage color images where, because of the implementation of basic steganographic techniques, it is possible to add information, exercises, questions, and so on. The exercises editor allows to decide which information must be visible or remain hidden to the user, when the image is loaded in the exercises viewer. Therefore, it is possible to hide the solutions of the proposed tasks; this is very useful to complete a self-evaluation learning process. These tools constitute a learning architecture with the final objective that learners can apply and practice new concepts or skills.

Aplicaciones de procesamiento de lenguaje natural

El campo de procesamiento de lenguaje natural (PLN), ha tenido un gran crecimiento en los últimos años; sus áreas de investigación incluyen: recuperación y extracción de información, minería de datos, traducción automática, sistemas de búsquedas de respuestas, generación de resúmenes automáticos, análisis de sentimientos, entre otras. En este artículo se presentan conceptos y algunas herramientas con el fin de contribuir al entendimiento del procesamiento de texto con técnicas de PLN, con el propósito de extraer información relevante que pueda ser usada en un gran rango de aplicaciones. Se pueden desarrollar clasificadores automáticos que permitan categorizar documentos y recomendar etiquetas; estos clasificadores deben ser independientes de la plataforma, fácilmente personalizables para poder ser integrados en diferentes proyectos y que sean capaces de aprender a partir de ejemplos. En el presente artículo se introducen estos algoritmos de clasificación, se analizan algunas herramientas de código abierto disponibles actualmente para llevar a cabo estas tareas y se comparan diversas implementaciones utilizando la métrica F en la evaluación de los clasificadores.

An explanatory model of academic achievement based on aptitudes, goal orientations, self-concept and learning strategies

As a result of studies examining factors involved in the learning process, various structural models have been developed to explain the direct and indirect effects that occur between the variables in these models. The objective was to evaluate a structural model of cognitive and motivational variables predicting academic achievement, including general intelligence, academic self-concept, goal orientations, effort and learning strategies. The sample comprised of 341 Spanish students in the first year of compulsory secondary education. Different tests and questionnaires were used to evaluate each variable, and Structural Equation Modelling (SEM) was applied to contrast the relationships of the initial model. The model proposed had a satisfactory fit, and all the hypothesised relationships were significant. General intelligence was the variable most able to explain academic achievement. Also important was the direct influence of academic self-concept on achievement, goal orientations and effort, as well as the mediating ability of effort and learning strategies between academic goals and final achievement.