Instrumentalização pedagógica para avaliação de crianças com risco de dislexia

Pós-graduação em Educação - FFC

Desempenho oral em português para falantes de outras línguas: da avaliação à aprendizagem de línguas estrangeiras em contexto online

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Dificuldades de aprendizagem da escrita em escolares de 1a a 4a séries do ensino público

This study aims to characterize and to compare the students writing difficulties from 1st to 4th grades of public education. The study included 80 students divided into GI (20 students from 1st grade), GII (20 students from 2nd grade), GIII (20 students from 3rd grade) and GIV (20 students from 4th grade). The procedure applied was the Assessment of Learning Difficulties in Writing. The results revealed a median learning difficulty in students of GI (greater average of errors in writing). The increasing of schooling determine a decrease of the average of these errors from GI to GIV. The study revealed that the students are still in a process of construction and appropriate of the writing system, since the average of writing errors decreased as the school level increased.

The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.